case study in german

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Translation of case study – English-German dictionary

(Translation of case study from the PASSWORD English–German Dictionary © 2014 K Dictionaries Ltd)

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relating to the scientific study of animals, especially their structure

Dead ringers and peas in pods (Talking about similarities, Part 2)

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German Cases Explained, Plus Memorization Tips and Practice Resources

First, let’s address the big question: What is a case?

In any language,  a case is a  way to show how a word integrates into a sentence . It’s kind of like looking at a schematic of a building and figuring out how the floors, stairs, rooms and hallways fit together.

There are four German cases: nominative, accusative, dative and genitive. Most German sentences include at least one case, but it’s rare that you’ll see all four cases in a single sentence.

Knowing your cases is vital in German, as many words change depending on what case they are in. 

So without further ado, let’s learn the German cases!

German Cases Explained

Tips for learning the german cases, memorize declensions and genders, understand the various uses of the nominative.

• Know the difference between direct and indirect objects

Learn the 31 most common prepositions

Resources for practicing german cases, and one more thing....

Download: This blog post is available as a convenient and portable PDF that you can take anywhere. Click here to get a copy. (Download)

The nominative case is first on our list because it’s typically first in the sentence, and it’s the case you’ll probably get the most practice in.

In this section, we’ll focus on the most common use, where the nominative case is the subject of the sentence.

(As you move up in fluency and create more complex sentences, you may use entire phrases as the subject in the nominative case. And later we’ll see examples of a double nominative, or no nominative at all—but don’t worry about these yet!)

When you start learning German, you often begin sentences with ich (I). For example:

Ich heiße… ( I’m called…)

Ich bin… ( I am…)

In both of these phrases,  ich is in the nominative case, as the sentence’s subject. Here’s another example:

Sarah wohnt in Berlin. ( Sarah lives in Berlin.)

Here, Sarah is the subject of the sentence and so is in the nominative case. Notice that the verb wohnen (to live, reside) is conjugated in the er/sie/es (he/she/it) form, since we’re talking in the third person. 

That leads us to two tips for recognizing the nominative case:

• Verb conjugation. The verb will typically be conjugated in relation to the subject of the sentence, and the subject of the sentence will be in the nominative case.
• Definite and indefinite articles. The nominative case will only use der ,  die  or  das for definite articles, and ein or eine for indefinite aricles.

If either of these characteristics doesn’t fit, we move on to the next candidate—the accusative case.

The accusative case is typically used for the direct object of the sentence , though there are accusative prepositions and accusative pronouns as well.

The accusative occurs almost as often as the nominative case . What’s great about the accusative case is that it’s pretty easy to identify:

• Start by looking for multiple nouns and/or pronouns within the sentence.
• Determine which noun/pronoun is being used with the conjugated verb—this one is in the nominative case.
• Any remaining noun(s)/pronoun(s) are in a different case.
• Look at the verb again to see the relationship it has to the remaining noun(s)/pronoun(s). If they receive the action of the verb, they’re direct objects and qualify as the accusative case.

Looking for a shortcut? Take a look at the masculine noun(s)/pronoun(s) in your sentence. Do they include  den  or  einen ? For example:

Suzie kauft einen Apfel . (Suzie buys an apple .)

Here, the verb kauft (buys) corresponds with Suzie, who is the subject of the sentence and is in the nominative case. The buying action is being done to the apple, which makes it the direct object. And einen Apfel  is in fact in the accusative case!

Here are some more examples:

Sie kaufen den Käse und schneiden ihn . (They buy  the cheese  and cut  it .)

Did you notice the -n ending on the den before Käse? That’s a tell-tale sign of a masculine noun in the accusative case. In the second part of the sentence, the pronoun ihn  replaces  den Käse , also in the accusative case. 

Wir gehen durch den Wald . (We walk through the forest .)

In this example, durch is an accusative preposition, which means that any noun(s)/pronoun(s) following it will always be in the accusative case.

Have no fear—we’ll talk more about this later, and there’s plenty of places for you to practice identifying the accusative !

The German dative case is a bit less defined than the nominative or accusative cases. While the dative case usually occurs as the indirect object of a sentence, it may also show up as prepositions, verbs and pronouns as well.

Let’s look at an example:

Mein Bruder gibt seiner Freundin einen Ring. (My brother gives his girlfriend a ring.)

The brother is the subject in the nominative case and the ring is acted upon as the direct object in the accusative case. As the girlfriend is the recipient of the direct object (the ring), she is the indirect object in the dative case.

So what about the example below?

Mein Kopf tut mir weh. (My head hurts (me) .)

This is where it gets tricky. The verb wehtun (to hurt) is separable and takes a dative object. We say “My head hurts” in English, but in German, you need to state who feels the hurt—me, in the dative case.

If you’ve ever seen or used the verb  sich waschen (to wash oneself), you’ll understand why:

Ich wasche mich. (I wash myself.)

Ich wasche mir die Haare. (I wash my hair.)

When used alone,  sich waschen  is an accusative reflexive verb. However, when we add an object to the sentence, the reference  mich turns from the accusative case to the dative case as mir .

It’s worth taking some time to learn which verbs are always dative . For instance, if someone says something to you, it’s always dative:

Ich habe dir gesagt, dass du um zehn Uhr nach Hause kommen sollst. (I told you to come home at ten o’clock.) 

The dative case isn’t always so complicated though. In fact, there are plenty of prepositions which take the dative case, such as mit  (with, by means of):

Wir fahren mit der Bahn nach Italien. (We’re traveling to Italy  by train .)

So, how can you spot the dative case in action? Go back to those definite and indefinite articles.

Nouns and pronouns in the dative case will change from der , die and das to dem , der and dem , respectively, while plural nouns and pronouns will change from die to den . Indefinite articles reflect the same endings: -em , -er , -em and -en .

Be careful here, because it can be easy to confuse the plural  den  with the accusative masculine  den . Don’t fall for that red herring!

Last but certainly not least, the genitive is typically used to show possession.  Like many of the German cases, the genitive may also appear in prepositions, verbs and pronouns.

Consider this example sentence:

Das Handy meines Bruders ist kaputt. ( My brother’s cell phone is broken. / The cell phone of my   brother is broken.)

To get the genitive down, it’s helpful to remember that “of” part of the phrasing.

For instance, think “the floor of my bedroom” instead of “my bedroom floor.” Or “the song of my people” instead of “my people’s song.” Or even “the eye of my mind” instead of “my mind’s eye.”

German also includes genitive prepositions, such as  außerhalb  (outside of):

Sie wohnt außerhalb der Stadt. (She lives outside of the city. ) 

And when possession isn’t easy to determine, look to your trusty confidential informants—definite and indefinite articles.

The genitive case is simple because its articles only use -es (masculine and neuter) and -er (feminine) endings. If you need help remembering this, try thinking of the phrase “his and hers.” The possessive pronoun “his” has an “s” while “hers” has an “r.”

Note that the genitive case is considered a fancy grammatical remnant of older German language. Some people think it’s more trouble than it’s worth, and it seems that it’s being used less and less .

But of course, it’s still good to be able to recognize and employ it!

In English, you always know where you stand with your articles. Regardless of gender, quantity or who’s doing what action, “the” will always be “the.”

German is far more specific. Memorize the declensions  (which tell you information about a noun’s case, number and gender) to help you recognize each case. Here are definite articles in German (English “the”), for example: 

Recognizing the forms of definite articles in a sentence will help you identify cases that much quicker.

Similarly, memorizing the gender of German nouns is essential, as you’ll be able to confidently assess which article is in use for those that have the same form.

The nominative case is the subject of the sentence. While that’s often just the person or thing doing the action, there are a few more complicated instances of the nominative case.

First, there’s the double nominative. If a sentence only uses some form of the verb sein (to be), then both nouns in the sentence are in the nominative case. For instance:

Die neue Studentin ist  eine Französin aus Paris. ( The new student is a French girl from Paris)

Das Praktikum war die beste Erfahrung meines Lebens. ( The internship was the best experience of my life.)

It makes sense if you think about it, because the sentence doesn’t actually have an object—it just has the same subject twice. However, keep in mind that, as with any rule, there are exceptions and this is not always the case 100% of the time.

Second, there are German sentences without the nominative case.

If you’re just starting German, you might be tempted to say “Ich bin kalt” to say “I am cold,” but instead of describing your temperature, you’re instead making yourself sound emotionally vacant or withdrawn. 

You should instead say “Mir ist kalt,” which literally translates into English as “To me is cold” but is the proper German way to express discomfort when the window is open. Here, there’s only one pronoun, and it’s in the dative case. You’re essentially saying that the environment is cold for you , rather than you just being cold.

Know the difference between direct  and  indirect objects

This will make it much easier for you to distinguish between the accusative and dative cases specifically. Keep in mind that:

• An object which directly receives the effect of an action and is the primary object of the sentence is a direct object. For example: “ Please write the essay .”
• An object which is passively affected by an action and is not the primary object of the sentence is an indirect object. For example: “Tell  him the news.” Note that here, “the news” is the direct object.

This will help you learn German cases because German prepositions take nouns of specific cases.

The most common prepositions can be defined by these four groups:

• Dative prepositions:  ab (away from), aus (out of, from), bei (at, near), mit (with), nach (after, to, according to), seit (since, for), von (from, of), zu (to), gegenüber (across from, opposite).
• Accusative prepositions: bis (to, up to, until), durch (through, by means of), für (for), gegen (against), ohne (without),  um (at, around (time)) and entlang (along).
• Genitive prepositions: außerhalb (outside of), innerhalb (inside of), jenseits (on the other side of, beyond), während (while, during), trotz (despite), and  dank (thanks to).

Dual prepositions:   an (to, on), auf (on, upon), hinter (behind), in (in, into), neben (next to), über (above), unter (under), vor (before, in front of) and zwischen (between, among).

For dual prepositions, it depends on whether the action you are describing is stationary or moving. 

If the subject of the verb is moving, then the noun will be in the accusative case, for example: 

Wir gehen in den Supermarkt.   (We go to the supermarket.) 

The action describes walking, so movement, so the masculine noun Supermarkt goes in the accusative case, so you need to use  den . 

If the subject of the verb is stationary, so not moving, then you put the noun after the preposition in the dative case:

Ich sitze auf der Bank .  (I’m sitting on the bench)

The action described is sitting still, so the feminine noun Bank  goes in the dative case, so you need to use  der . 

So, once you memorize some common prepositions, you’ll know exactly which case you need to use after it.

The best way to better understand German cases is to practice!

I highly recommend you start with diagramming sentences in German. Take your time to determine the case of each noun (pronoun, etc.) in your study sentences, and why they’re in that particular case. Eventually, you’ll start noticing the patterns on your own without having to diagram each sentence.

You can get sentences from a number of places, such as German textbooks , graded readers , TV shows , movies and more.

In fact, reading and listening to how native Germans use cases will help you get familiar with them. You may try listening to German audiobooks or YouTube videos , or you may want to try programs made specifically for language learners.

FluentU takes authentic videos—like music videos, movie trailers, news and inspiring talks—and turns them into personalized language learning lessons.

You can try FluentU for free for 2 weeks. Check out the website or download the iOS app or Android app.

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For additional practice and study materials, check out:

• Learn German —hear cases used in level-appropriate situations.
• German.net —practice the nominative case first, or select the area you need the most help with.
• German Prepositions Battleship —test your knowledge of prepositions and their cases by playing this classic game.
• Verbs with Prepositions —use this printout as a reference or commit it to memory.
• Easy Deutsch —fill in the correct pronouns in the quiz, using the genitive form as needed.

Once you have more experience recognizing German cases, you’ll be able to see the relationships of the words in those long German sentences.

And when you’re comfortable with the German cases, you’ll be able to start writing and speaking German with greater fluency. You got this!

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Genocide Studies Program

Between the ascension of the Nazi regime to power in Germany in 1933 and the defeat of the German army in 1945, over six million civilians perished at the hands of German forces, their military allies, and their civilian associates.   The word “Holocaust,” with Greek roots meaning “destruction of life by fire” and a common translation of the Hebrew word “Shoah,” is nearly universally understood to refer to these events.

The murderous effort rested on an ideology of racial superiority and aspirations of racial “purity.”  Jews were by far the largest component of the victims.  Roma and Sinti (both commonly – but sometimes derogatorily – referred to as “Gypsies”) were also targeted, as were the physically disabled, the mentally disabled, and members of several religious minorities.  Political opponents, as well as Slavic and Russian civilians, were also murdered in large quantities, although whether these mass atrocities constituted part of a genocide is less certain.

The Holocaust unfolded over time.  When the Nazi regime came to power, it was already imbued with an ideology of racial ideology – which happened to comport with its own sense of it political enemies.   It began establishing concentration camps shortly after coming to power.  The regime also systematically discriminated against Jews (and other groups it perceived to be racially inferior) in the economic, political, and civic realms.  November 1938 pogroms known as “Kristallnacht” demonstrated that the German police would tolerate (and, indeed, encourage) violence against Jews and their property.  Once World War II broke out the following year, the German government expanded its concentration camp system, and soon converted them into an infrastructure for mass killing.  Meanwhile, an even greater number of Jews and other civilians would be killed outside of the camps, either through the Nazi SS Einsatzgroppen (mobile paramilitary units) or the actions of Nazi supporters on either side of the front lines in Eastern Europe.

The Holocaust is undoubtedly the seminal event for the field of genocide studies.  Even as scholars examine new and different cases from a variety of perspectives, the foundation of the field lies in effort to understand the organization, behavior, and psychology of different actors  – those who killed, those who stood by, those who perished, those who attempted to help, and those who survived – the Holocaust. Many of the field’s most important scholars continue to address these issues today.

A central component of the Genocide Studies Program has been Dr. Dori Laub’s  study of trauma among Holocaust victims, which makes extensive use of Yale University’s Fortunoff Video Archive for Holocaust Testimonies.

Videotestimony Pilot Study of Psychiatrically Hospitalized Holocaust Survivors

Principal Investigator: Dori Laub , MD, Deputy Director (Trauma Studies), Genocide Studies Program. For more details please visit the Traumatic Psychosis: A Videotestimony Research Project website.

The purpose of this research is to systematically assess the effects and potential psychotherapeutic benefits of reconstructing traumatic Holocaust experiences. The reconstruction of the history of personal trauma were conducted through the creation of a videotaped testimony and a multi-disciplinary analysis of the testimony. This study addressed two hypotheses:

• Is massive psychic trauma related to chronic severe mental illness with psychotic decompensation that leads to either chronic hospitalization or multiple psychiatric hospitalizations?
• Does a therapeutic intervention such as video testimony that helps build a narrative for the traumatic experience and gives it a coherent expression help in alleviating its symptoms and changing its course? May these changes be attributed to direct intervention (through the occurrence of the testimonial event itself), or through indirect intervention (through the impact on treatment planning, involvement with family members or the survivor community, or the knowledge that the videotaped testimony will be made available to others)?

A 1993 examination of approximately 5,000 long-term psychiatric inpatients in Israel identified about 900 Holocaust survivors. These patients were not treated as unique: trauma-related illnesses were neglected in diagnosis and decades-long treatment. Evaluation by the Israeli Ministry of Health concluded some 300 of them no longer required inpatient psychiatric hospitalization; specialized hostels (similar to nursing homes) were established on the premises of three psychiatric hospitals. We hypothesize that many of these patients could have avoided lengthy if not life-long psychiatric hospitalizations, had they been able or enabled by their treaters and by society at large to more openly share their severe persecution history. Instead, their traumatic experiences remain encapsulated, causing the survivor to lead a double life: a robot-like semblance to normality with incessant haunting by nightmares and flashbacks. Attention to the particular features of these patients traumatic experiences is of particular importance in the rehabilitation and the re-evaluation of these patients whose initial hospitalization and diagnoses long predate more recent theoretical developments and clinical formulations (e.g., differential diagnosis of PTSD, testimony as therapy).

Phase II of the videotestimony study which is now underway, consists of an in-depth analysis of the videotexts by an interdisciplinary team of experts, in order to define the unique features of the traumatic psychotic disorder these patients most likely suffer from.

The Slave Labor Video Testimony Project

The Foundation for “Remembrance, Responsibility and Future” has organized an international project to collect 550 video and audio testimonies from former forced and slave laborers in the German “Third Reich.”  Ex-laborers from 25 different countries, mostly in Eastern Europe, are being interviewed. The project requested the GSP’s Trauma Project to conduct 20 videotestimonies with Jewish Holocaust Survivors in the United States. The names of these survivors were obtained through the Fortunoff Video Archive and through the Connecticut Child Survivor Organization. After proper preparation, the videotestimonies were filmed on the European PAL format and on the American NTSC format, in parallel with professional audiotaping. The testimonies were all given in English and lasted between two and four hours. All subjects also filled out a symptom checklist PCL-9 for Post-Traumatic Stress Disorder, which will be repeated within a year of their testimony to see whether the testimonial event has brought about possible symptom changes and symptomotology.

The twenty videotestimonies, taken in Dr. Laub’s office in New Haven, Connecticut, have all been completed and transcribed and translated into German. The PAL videocassettes were sent to an audio visual lab in Israel to be transferred to an enhanced BETA format.  After that enhancement, they were shipped to Hagen University in Ludenscheid, Germany, which coordinates this international study, along with their translated transcripts and the consent forms, as well as summaries. They were also sent to the Foundation for “Remembrance, Responsibility and Future.” This project has created a substantial database, useful for future historical, psychological and linguistic studies, for which definite funding is needed.

Dori Laub, Presentations 2005-2011

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Turnaround Cities: German Case Studies - Insights from Dortmund, Duisburg and Leipzig

Authors: Susanne Frick, Paula Prenzel

Organisations: The Government Outcomes Lab, Institut für Geographie und Geologie, Universität Greifswald

The international comparators project was a major study of 8 cities around the world that have turned themselves around from decline to success story. Each has been resilient in the face of waning industry, utilising policy instruments and collaborative initiative to bounce back. Authors Susanne Frick, Ian Taylor and Paula Prenzel worked with Professors Paul Collier, Philip McCann, Colin Mayer and Vincent Goodstadt to produce 3 case study reports covering 8 cities in 6 countries these case studies offer examples of policy innovation for regional development that are instructive to the UK in its efforts to correct spatial inequality and for other countries looking to weather post-industrial decline.

The German cities of Duisburg, Dortmund and Leipzig are examined in detail by Dr Susanne Frick, based at the University of Oxford, and Dr Paula Prenzel, based at the University of Greifswald. Germany is renowned as an example of successful economic strategy across devolved levels of government.

| SOC21 | Social levelling-up: the role of cross-sector partnerships, place and devolution in addressing social disparity between regions

Turnaround cities: anglo-saxon case studies - insights from pittsburgh (pa), newcastle (nsw) and windsor (ont.), turnaround cities: western europe case studies - insights from lille, france, and the basque country & bilbao, spain.

Cases in German: Easily Explained

Outline of the article.

1: Introduction to Cases in German       2: Overview of the Four German Cases            2.1: Nominative Case            2.2: Genitive Case            2.3: Dative Case            2.4: Accusative Case       3: The Role of Cases in German Grammar       4: Declension of German Articles            4.1: Definite Articles            4.2: Indefinite Articles       5: Nominative Case in Detail            5.1: Function            5.2: Examples       6: Genitive Case in Detail            6.1: Function            6.2: Examples       7: Dative Case in Detail             7.1: Function             7.2: Examples       8: Accusative Case in Detail             8.1: Function             8.2: Examples       9: Using Cases with German Prepositions             9.1: Dative Prepositions             9.2: Accusative Prepositions             9.3: Two-Way Prepositions      9.4: Genitive Prepositions       10: Cases and German Pronouns       11: Tips for Mastering German Cases       12: Common Mistakes to Avoid       13: Conclusion       14: Frequently Asked Questions (FAQs)

Introduction to Cases in German

Learning German involves mastering its cases. The German language has four cases: Nominative, Accusative, Dative, and Genitive. Understanding these cases is crucial for proper communication. This article will explore the four German cases, their role in grammar, and how to use them effectively.

German Cases Chart

Here you can see a chart of the four cases in German. At the beginning of this article, you can read in short about the cases. Later on, you get a detailed explanation with examples for each case. 

Nominative Case

The nominative case marks the subject of a sentence. which is the person or thing performing the action. You can ask who or what to find the nominative.

Genitive Case

The genitive case shows possession or relationships between nouns.

Dative Case

The dative case indicates an interaction between the subject and the object.

Accusative Case

The accusative case does not have any interaction between the subject and the object.

The Role of Cases in German Grammar

In German grammar, cases play a vital role in determining the function and relationship of words within a sentence. By correctly applying cases, you can ensure that your sentences are clear, accurate, and easy to understand.

Cases can alter the form of nouns, pronouns, and even adjectives, depending on their function in a sentence. This process is called declension. For example, German articles (both definite and indefinite) and personal pronouns change their form based on the case they are in. As a result, mastering cases and declensions are crucial for anyone learning German.

Understanding cases also help you using prepositions correctly. In German, specific prepositions require the use of certain cases. Knowing which case to use with each preposition is essential to form accurate and meaningful sentences.

Declension of German Articles

First, you can get information about the declension of definite and then infinite articles in the German language.

Definite Articles

German definite articles change according to the case they're in. For example, "der" (masculine) becomes "den" in the accusative case and "dem" in the dative case.

Indefinite Articles

Indefinite articles also change depending on the case. "Ein" (masculine) changes to "einen" in the accusative case and "einem" in the dative case.

Nominative Case in Detail

The nominative case is used for the sentence's subject, the one performing the action. You can ask who or what to figure out the nominative.

German Nominative Quiz

Would you like to practice your knowlege about the Nominative in German in quizzes ? You can find the newest German Nominative quiz: here . 

Genitive Case in Detail

Dative case in detail.

The dative case is used if there is no interaction between the subject and the object. You can ask "to whom or for whom"to figure out the dative case.

As a German, you learn the question: "wem oder was" (whom or what) to see a dative. The difficulty is that the accusative question: "wen oder was" has the same translation (whom or what),Many grammar books are referring to dative as the indirect and accusative as the direct object which can make it very confusing to understand the differences of dative and accusative.

Accusative Case in Detail

In the accusative case, there is an interaction between the subject and the object. Sometimes it can be difficult to figure out the accusative and dative. 

One advanced trick to finding an accusative is to form the sentence from active to passive. In a passive clause, the accusative becomes the subject in a sentence, and the nominative the dative object. 

In the sentence: Die Mutter gibt der Tochter das Geschenk. The passive sentence would be: Das Geschenk wird der Tochter von der Mutter gegeben. You can see that the accusative became a nominative and that the dative still is dative.

German Accusative Quiz

Did you understand the accusative in German? That is great! If you are not sure, you can find the latest German accusative quiz: here . 

Using Cases with German Prepositions

Prepositions with Dative      

These are the most popular prepositions with dative. After a dative preposition comes a dative. You can read more about the German dative prepositions in detail: here. 

Das Kind geht mit der Mutter in das Kino. = The child goes with the mother to the cinema.  When you see one of the dative prepositions then you know that after this stands always a dative. 

Prepositions with Accusative

Das Kind kocht für die Mutter das Essen. = The child cooks the food for the mother.  When you see one of the accusative prepositions then you know that after this stands always an accusative. 

You can read more about German accusative prepositions: here .

German Two-Way Prepositions (Wechselpräpositionen)

The German two-way prepositions can be used with both dative and accusative cases. To determine whether it is a dative or accusative case, you can apply these rules:  

• Where? = Dative  Example: Das Mädchen ist in der Schule. The girl is in school. Where is the girl? She is in school. When you can ask for "where" then the dative is needed.       
• Where to? = Accusative  Example: Das Mädchen geht in die Schule. The girl goes to school. Where does the girl go to? She goes to school. When you can ask for "where to" then the accusative is applied. 

You can read more about the German two-way prepositions in detail: here . 

German Two-Way Prepositions: Free Quiz

You can find a free Grammar quiz for the topic Two-Way Prepositions in German: here . 

German Genitive Prepositions

These genitive prepositions are the most common ones. They are normally used with a genitive, but in the spoken language people tend to use a dative for some of these prepositions instead.

Cases and German Pronouns

As you can see in the chart German personal pronouns also change according to the case. For example, "ich" (I) becomes "mich" in the accusative case and "mir" in the dative case.

Tips for Mastering German Cases

• Study the cases and their functions.
• Learn the declensions of articles, nouns, and adjectives.
• Practice with German prepositions.
• Learn which prepositions and verbs are used with which case.
• Practice writing and focus on the cases while doing it.
• Do as many exercises as possible.

Common Mistakes to Avoid

• Confusing the dative and accusative cases.
• Misusing prepositions with specific cases.
• Mixing up pronoun declensions.
• Not knowing two-way prepositions

German cases are an essential part of the language. By understanding the four cases (Nominative, Genitive, Dative, and Accusative) and their roles in German grammar, you can improve your German skills and gain a deeper understanding of the German language. Practice is the key to mastering cases in German.

Frequently Asked Questions (FAQs)

What are the four German cases?

The four German cases are Nominative, Genitive, Dative, and Accusative.

Why are cases important in German?

Cases are important because they help identify the role each word plays in a sentence, making communication clearer and more precise. You need the cases for every sentence and many other grammar topics are connected with the cases like the German articles or adjective endings.

How can I learn the German cases?

Make sure that you understand the rules of the cases in German. Learn as much as possible and try to write and speaking a lot while you focus on the cases.

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German cases

In these exercises, you will be required to use all four of the German cases. Continuous practice will help you get good at using and identifying the cases. This is one of the greatest difficulties in learning German, but it is also a crucial element of the language.

As you work, think carefully about all the ways that cases are marked and reflected, including adjective endings, pronoun choices, article endings, additional noun endings, and word order.

German translation of ' case study'

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• Open access
• Published: 15 September 2022

European road transport policy assessment: a case study for Germany

• Michael Schulthoff 1 ,
• Martin Kaltschmitt 1 ,
• Christoph Balzer 2 ,
• Karsten Wilbrand 2 &
• Michael Pomrehn 2  

Environmental Sciences Europe volume  34 , Article number:  92 ( 2022 ) Cite this article

4947 Accesses

1 Citations

Metrics details

In the EU, the transport sector is the only sector with increasing GHG emissions compared to 1990. While harmful emissions have decreased due to successful regulation, transport performance, fossil fuel consumption and thus CO 2 emissions have continued to increase, despite powertrain efficiency improvements. Meaningful regulation, which can be market-based (MBI) and non-market-based (NMBI) by nature, is needed to meet climate targets. To understand the mechanisms, effects and limitations of MBI and NMBI, this study investigates and evaluates selected regulations in the German road transportation sector until 2020. Therefore, this study identifies, describes, and categorizes environmental policy instrument types. Based on this step, selected instruments in the road transportation sector are identified by their type and implemented policies are described and assessed. Furthermore, an assessment methodology is developed to evaluate and score target achievement, cost-efficiency and practical feasibility by linking the outcomes of instruments to its goals. Based on the findings of this assessment, conclusions and recommendations are developed and discussed. Finally, results and general properties of policies and their type of instruments are extrapolated, and general statements about market and non-market-based instruments in a broader context for future regulation and market designs are projected. The study discovers that fuel producers and distributors, vehicle manufacturers and sellers are directly regulated by non-marked-based instruments, despite the EU Emissions Trading Scheme (ETS). On the customer side, primarily market-based implemented except for low-emission zones, which are direct regulations. The study finds that holistic representation and realistic internalization of external effects in a market is complex and will never be complete. Still, sufficient representation can be enough to drive transformation in the transport sector. The CO 2 price itself is not sufficiently representing the consequential costs of climate change induced by road transport, but it helps to make low-carbon alternatives economically viable. Overall, the study finds that most implemented regulations in the German road transport sector were successful in relation to their goals.

Introduction

Burning fossil hydrocarbons releases energy by forming, among other airborne substances, carbon dioxide (CO 2 ). This combustion product is released as waste into the atmosphere. The vast amount of CO 2 produced by human activities throughout the last 150 years is slowly causing an imbalance in the earth´s climate system, resulting in climate change. This change can create significant threats to nations, societies and—apart from other environmental damage—human existence. Therefore, the global society has decided to limit additional fossil fuel-based CO 2 emissions to keep global warming well below 1.5 °C relative to pre-industrial times.

As a consequence, societies need to transition toward a more sustainable and CO 2 -neutral energy system. A wide spectrum of very diverse policy instruments (EPI) has been developed in recent years to address the various sources of climate change and to move our society toward less GHG emissions. These policy instruments are based on various principles—such as polluter pays or prevention principles—and differ in their way of operation.

While within the European Union (EU), the power and industrial sector decreased their CO 2 —and other harmful—emissions during the last decades, the road transportation sector emission levels have steadily increased. But also, road transportation capacity and individual passenger mobility demand rose during this timeframe. Therefore, improvements in efficiency have been fully compensated in terms of the overall CO 2 emissions from this sector. In addition, projections show that especially the commercial road transportation demand will continue to grow within the EU.

To address this development and to force the transportation sector to contribute to CO 2 mitigation, well-designed environmental policy instruments need to be applied to accelerate the shift to low- or even zero-carbon transport technologies and behaviors. Within this context, assessing such instruments already applied within the transportation sector within the EU is fundamental.

The present study identifies, describes, and categorizes environmental policy instrument types (see Additional file 1 ). Based on this step, applied instruments in the road transportation sector are identified by their type and implemented policies are described and assessed. Therefore, an assessment methodology is developed to evaluate and score target achievement, cost-efficiency and practical feasibility. Based on the findings of this assessment, conclusions and recommendations are developed and discussed. Eventually, results and general properties of policies and their type of instruments are extrapolated, and general statements about market and non-market-based instruments in a broader context for future regulation and market designs are projected.

Assessment of selected policies

Brief profile of road transportation in germany.

Road transport has a significant impact on the climate. The German transport sector emitted 164 Mil. tons CO 2 eq in 2019 (pre-COVID), representing 20% of German GHG emissions. 160 million tons of CO 2 eq were produced alone by the road transport sector (see Additional file 1 : 10 and 11). Compared to 1990, the transport sector has shown no total reduction in GHG emissions, while its share increased by 7% of total German GHG emissions [ 74 ]. The final energy demand (FED) from transport slightly decreased from 655 TWh in 1990 to 638 TWh in 2019, a reduction of 2.6%. In 2019, the road transport sector represented 592 TWh representing 93% of transport sectors FED (see Additional file 1 : 12 and 13) [ 38 , 64 ]. The number of registered passenger cars steadily increased from 30,6 Mil. in 1990 to 47,1 Mil. Cars in 2019 (2021: 48,3 Mil.) in Germany [ 66 ]. Furthermore, the average power of cars increased by 66% between 1995 and 2019. Cars became 11% heavier due to a rising share of sport utility vehicles (SUV) [ 47 ]. In 2019, 98% of cars were fueled by petrol (66%) and diesel (32%), while alternative powertrains were only 2% (see Additional file 1 : 12) [ 1 , 56 ]. More supporting statistics are shown in Additional file 1 in chapter 3. The data indicate that Germany's road transportation sector has not moved toward climate neutrality since 1990. Similar behavior can be observed in other European countries. Therefore, a bouquet of various environmental policy instruments was implemented to reduce the transport sector's impact on tackling the road transport sector. Further policy and revisions are yet to come due to the “fit for 55” package.

Selected policies

Different environmental policy instruments have been applied to realize environmental protection within this sector [ 55 ]. Some instruments focus on mitigating relative or specific emissions or performance standards, while others aim to reduce the overall absolute emission amount. Figure  1 places the regulations applied in the context of total emission reductions. Total emissions are a product of the total fuel demand and the emission intensity of the respective vehicles. Therefore, reducing total emissions can be achieved by mitigating one or all of those factors. While the fuel demand depends on user behavior, the emission intensity is related to the fuel type and the respective vehicle specifications. Each of these factors can be addressed with different environmental policy instruments to meet the set goals.

Relation of policy instruments in 2020 in road transport according to the total emissions

For example, the German government regulates—partly based on EU Directives—the transport sector by a spectrum of environmental policy instruments with overall 42 policies in 2021 [ 53 ]. Table 1 summarizes the central policies in road transportation in Germany. Non-market-based instruments are mainly applied for minimizing harmful emissions from fuels and exhaust gases (burning products). Market-based instruments are additionally used for emission reductions, e.g., by vehicle taxation—based on vehicle properties—and consumption of fuels. These instruments are listed by their objective downward the supply chain. Notably, non-market-based instruments are preferably applied to producers and distributors, whereas market-based instruments are chosen for consumer regulation. From a lifecycle assessment standpoint, cradle-to-gate impacts are regulated by non-market-based instruments, as markets do not fully internalize all relevant (external) effects for a holistic assessment. ETS certificates required to produce fuels and vehicles are not mentioned, because this is not a road transportation-specific mechanism.

Thus, CO 2 emissions are regulated by different instruments in Germany. On the one hand, CO 2 is controlled passively by preferences of the fuel, CO 2 fleet limits and car labeling. On the other hand, market-based instruments policed by vehicle, energy and CO 2 taxes are based on the polluter-pays principle. Consequently, the transportation sector has a higher implicit CO 2 price compared to the explicit costs for CO 2 within the ETS [ 45 ].

Evaluation criteria

Environmental policy instruments affect different areas of the economy, environment, and society. Therefore, the evaluation of such instruments is based on the assessment of target achievement, cost-effectiveness, and practical feasibility [ 45 ], adopted from the EU environmental policy evaluation. Relevant studies and data sets are investigated for each criterion to emphasize key performance points.

Target Achievement measures whether and to which degree a policy achieves the set objectives or goals. A policy measure serves different objectives, although a hierarchy of the different objectives is not necessarily discernible. Emissions reduction is often the explicit or implicit goal of such an environmental policy instrument. Explicit regulations target the issue directly, while implicit policies target the reduction by regulating an objective related to emissions. Results of target achievement can be different: Policy outcomes (e.g., laws and directives issued), outputs (e.g., the share of biofuels), and impacts (e.g., mitigation of risks resulting from climate change).

Cost-efficiency relates the inputs (cost) and results (effects) of policy intervention. It provides an economic metric for efficiency and helps to compare different instrument types [ 45 ]. The efficiency can be related to microeconomic cost (e.g., implementation cost for technology from a producer/consumer perspective) or macroeconomic cost (e.g., the social cost of health issues induced by air pollution). Furthermore, micro- and macroeconomic costs can be compared to each other. Efficiency is divided into its static and dynamic components. Static efficiency reflects the most favorable avoidance variant at a given time (low-hanging-fruit principle; see Additional file 1 : 1.6). The dynamic efficiency considers an intervention over a certain period. As a future-oriented criterion, dynamic efficiency necessarily contains an element of uncertainty. Since the cost and profitability of new technologies cannot be forecasted with certainty, it will also be difficult to accurately determine the efficient level of investment in low-carbon technology innovation and diffusion. To assess the cost-effectiveness of policy measures, the cumulative net costs discounted over time need to be considered. In a macroeconomic framework, this also includes the opportunity costs of investment [ 11 , 45 ]. In this analysis, the cost-efficiency represents the magnitude of the cost impacts of a measure in relation to their achieved targets, based on studies made for each applied instrument.

Practical feasibility is a very heterogeneous criterion set. It summarizes other evaluation criteria associated with the instruments and the field applied. The various criteria thus refer to the difference between policies as designed on the drawing board and their actual implementation in practice. Therefore, they are grouped under the heading of feasibility. The spectrum of possible sub-criteria can include administrative implementation (e.g., reporting and review effort), unintended side effects, political acceptability, legal and institutional feasibility, flexibility, risk, and uncertainties [ 45 ].

Each environmental policy instrument reviewed with the criteria defined in Sect. 2.3 is scored in a three-step range. The applied spectrum is presented in Table 2 . As the criterion practical feasibility is heterogeneous, the scoring is related to its impact on the relevant sub-criteria. The evaluation takes place from the regulator's perspective or with a focus on the impact on society. For this purpose, the micro-economic costs (private-sector) incurred are placed in context with macroeconomic costs. Since the objectives of the environmental policy instruments (EPI) differ by their type, no overarching quantified assessment can be made here, either on an absolute or a relative scale. In some cases, several EPIs pursue the same reduction targets and changes, but the respective share of each EPI cannot be reliably determined. For example, CO 2 tax and energy tax both directly aim at a decline in demand and, therefore, indirectly at emissions reduction.

Moreover, the technological development (implied by the market pressure) toward higher efficiency, lower fuel consumption—and thus low emissions—is naturally brought about by lower operating costs. Reduction of CO 2 is thus automatically enforced by the market if fuel prices are sufficiently high. In contrast, reducing harmful emissions—covered by Euro standards—does not entail any cost savings for the user.

Evaluation profiles of policies in Germany´s road transportation sector

In the following, the applied instruments in the German road transportation sector are evaluated. Therefore, each instrument (shown in Table 1 ) is described, its targets are identified, the performance related to the criteria (shown in 2.3) is evaluated, and each performance key point is scored (based on Table 2 ).

Fuel quality directive (FQD)

Description.

The Fuel Quality Directive (FQD) and its amendments establish environmental requirements for gasoline and diesel fuels to reduce their emissions of air pollutants issued by the EU. The FQD combined with the Renewable Energy Directive (RED II) set standards for environmental emissions. The RED II targets to increase the share of renewable energy consumption mix by 32% by 2030, and the member states require fuel suppliers to include a minimum of 14% renewable energy consumed in rail and road transport. Besides the Fuel Quality Directive (FQD) Implementing Directive, the ILUC Directive and the “Winter Package” contain further the regulations for fuel suppliers [ 25 ].

The targets of the fuel quality directive until 2020 can be summarized as follows [ 25 , 33 ]:

Contribute to enhanced air quality

Contribute to greenhouse gas (GHG) reduction and biofuels sustainability. Reduction of the average life cycle GHG intensity of transport fuels brought into the market by a minimum of 6% by the end of 2020 (Indirect land-use change (ILUC) is not taken into account)

Reduce impacts on health and environment from transport fuels

Reduce greenhouse gas and air pollutant emissions from the transport sector

Ensure proper functioning of engines and after-treatment systems

Guarantee the quality of petrol and diesel

Ensure a single market for fuel (setting minimum standards for selected specifications)

Performance and score

In the following, the performance of key aspects for the selected criteria is summarized and shown in detail in Table 3 .

Most of the targets set for the FQD have been achieved, although the total GHG reduction targets have only been met partly or not at all. For example, only 62% of the life cycle GHG intensity reduction goal of 6% has been completed within the EU (excluding ILUC). Furthermore, no absolute CO 2 reduction was achieved by the FQD; instead, CO 2 emissions increased due to an increased demand for fuels. However, the FQD does not directly handle fuel demand (see Fig.  1 ). The CO 2 intensity reduction of 3.1% by 2020 has led to a less pronounced increase in emissions and is, thus—despite missing the target—to be assessed as positive and in line with the policies overarching goals [ 15 , 22 ].

The cost-efficiency assessment is primarily based on an evaluation of the European Commission from 2017. As the FQD is the only instrument directly aiming at the GHG intensity of fuels, the related costs cannot be compared and set into context with similar measures. The RED indirectly aims to reduce GHG intensity by setting goals for renewable energy shares. The costs mainly occur at a micro-economic level, which means at the industry or consumer level (unless the monitoring costs), while the benefits are at a macro-economic level. The occurring costs for the fuel distributors are at a much lower magnitude than the benefits for society due to avoided damage costs from harmful emissions.

The FQD is considered practically, as the intervention is mostly coherent with existing measures, except for inconsistencies of biofuels with the Renewable Energy Directive (RED). Without the FQD, promoting a single market for producers could not have been ensured.

While the FQD regulates the fuel supply side, emissions standards are a common instrument regulating emissions from vehicles that manufacturers must abide by. In Europe, two types of emission standards are applied. Non-CO 2 emissions are limited by the Euro norms for all road vehicles (as described in the following), whereas the CO 2 fleet limits regulate CO 2 emissions of passenger cars and LDVs [ 30 ].

The Euro norms are emission standards for passenger cars and commercial vehicles for motor vehicles and their specific replacement parts [ 25 ]. The policy aims to protect air quality, (indirectly) improve fuel economy, and encourage technological development and innovation [ 30 ]. The policy covers a wide range of tailpipe, evaporative and crankcase emissions. Thus, in-cylinder and after-treatment technologies were developed and implemented. Exclusively, harmful pollutants are regulated: carbon-monoxide (CO), non-methane hydrocarbons and total hydrocarbons (C n H m ), nitrogen oxides (NO x ), particulate matter (PM) and particle number (PN) [ 25 ].

Table 4 describes the evolution of Euro Norms for light-duty vehicles. Several technologies to mitigate pollution became mandatory, and as the test procedures changed, real driving emissions were included. Emissions are measured in the use of a vehicle on the road. The International Council of Clean Transportation (ICCT) provides further studies on the emission standards compliances costs for diesel LDV and HDV, including estimated costs for EURO 7 [ 59 , 60 ].

As these tests are hard to reproduce on the road, real driving emissions were higher than those reported in laboratory tests, resulting in a confirmation factor by the EU that relates laboratory and real driving emissions [ 2 ]. The EU limits air pollution to mitigate cardiovascular diseases and premature statistical deaths. The World Health Organization (WHO) provides its Air Quality Guideline recommendations for outdoor and indoor pollution limits. The WHO defines the limits on several short- and long-term exposure studies investigating exposure–effect relationships. Lower limits are based on the so-called NOAEL (no observed adverse effect level), which shows no related effect of pollutants on health issues. Especially, particular matter shows a harmful impact on health with every pollution level and is associated with the particular size. Limit values result from a political balancing process, whereas health improvements versus feasibility and costs of the actions are weighted [ 63 ].

In 2017, WLTP and real-driving emission tests were introduced to improve testing procedures for passenger cars. New cars must pass these tests in real driving conditions and improved laboratory tests before receiving approval for European roads. Euro IV became mandatory for heavy-duty vehicles in 2013 [ 19 ]. Since 2019, newly produced trucks must determine and declare their CO 2 emissions and fuel consumption with the latest version of the Vehicle Energy Consumption calculation Tool (VECTO) which was developed by the European Commission [ 34 ]. Furthermore, real driving emissions are tested by the verification testing procedure (VTP) to verify emissions and fuel consumption from July 2020 [ 30 ].

The implementation of the Euro standards generates additional costs due to the mandatory technologies to reduce emissions to achieve the targets. The costs were estimated by the ICCT in 2012 and are shown in Table 5 . According to Euro 1, gasoline cars were required to switch from carburetor to electronic fuel injection systems and install catalytic converters, which are more complex and more costly. From Euro 2, the costs for emissions reduction in diesel LDVs to meet regulations were always higher than for gasoline LDVs. Especially the mandatory installation of particulate filters (Euro 4) and selective catalytic reduction catalysts (SCR) led to increasing costs for diesel vehicles. The average car price in 2012 was 30,000 $, which resulted in a cost-share of 1% for gasoline and 5—6% for diesel LDVs [ 42 ]. However, the introduction of Euro 6 made gasoline particle filter necessary for some cars to meet the regulations, which implied costs for the transition of Euro 5 to Euro 6 for gasoline cars. These—not mandatory, but for some cars necessary—particle filter costs are not estimated in the cited study.

The targets of the Euro norms can be summarized as follows [ 13 , 52 , 77 ]:

Lower (harmful) air pollution from vehicles, improve air quality

Set fleet-wide performance standards (CO, NO x , SO x , C n H m , PM, PN) for the type of engine (diesel, gasoline) and vehicle type (car, LDV, HDV, motorcycles)

Indirect GHG emissions reduction (Euro 7 might include direct regulation of methane evaporation from CNG/LNG vehicles)

Implementation of representative and standardized laboratory test cycles and measure real driving emissions (mandatory for Euro 6)

In the following, the performance of key aspects for the selected criteria is summarized and broken down in Table 6 .

The Euro norms emissions standards achieved almost all of the set targets. It led to a significant reduction of harmful emissions (see SM2). Representative test cycles were implemented and constantly adjusted, but those test cycles do not fully represent real-world behavior. To account for this, real driving emissions (RDE) were introduced with the Euro 6 in 2014.

The cost-efficiency is hardly evaluated. Each new regulatory level requires certain technologies to meet the targets or are mandatory (see Table 3 ), which creates additional costs for emissions treatment. On the one hand, the microeconomic costs for customers for LDVs are shown in Table 5 and are 1–6% of car price (related to an average car price of 30,000 $). On the other hand, the macroeconomic avoided damage costs are estimated at 8,611 Mil. € only by reducing NO x .

Unified test cycles ensure comparability and make the policy practically feasible, but laboratory testing does not represent real-world emissions. Furthermore, real emissions are related to user behavior. This instrument has been implemented in several countries since 1990 (see SM9) [ 13 ].

CO 2 fleet limit

CO 2 fleet limits define performance standards for specific CO 2 emissions per base unit (distance [g/km] for passenger cars and commercial vehicles below 3500 kg and engine energy output [g/kWh] for heavy-duty vehicles). The base units are chosen differently, because those are related to the performance of a vehicle type. As the performance of passenger cars and light-duty vehicles is mobility itself, performance defined for heavy-duty is related to the transport capacity of a wide range of vehicle sizes. In 2020, the limits are 95 g CO2 /km for cars and 147 gCO 2 /km for vans while targeting a 15% reduction for cars and vans in 2025 based on 2021 starting points [ 7 , 28 ].

Table 7 summarizes the CO 2 fleet limits for cars and vans. As the fleet limits become more restrictive, a technology shift is indirectly induced, because standard internal combustion engines (ICE) cannot meet the emerging requirements. ICEs can meet those goals if they are verifiable fueled (blended or fully) with low-/zero-carbon fuels, such as biofuels or synthetic fuels (E-Fuels). However, even if the verifiable use of low-carbon fuels is accepted for the CO 2 fleet limits, the vehicles still have to meet the Euro norms regarding harmful emissions from combustion. At the time of conduction of this study, the discussion has not been solved. The EU annually sets specific emission targets for each manufacturer based on the EU fleet-wide average mass of the producer's new vehicles registered with a limit value curve [ 27 ].

Figure  2 illustrates the specific CO 2 emissions of newly registered passenger cars in Europe between 2000 and 2019. The blue line shows a constant reduction in gCO 2 /km during the NEDC test cycle. With the introduction of the WLTP test cycle, specific CO 2 emissions increased slightly due to a more demanding and realistic driving cycle. Until 2006, no mandatory regulations were set, and the voluntary agreements of car manufacturers only led to a reduction of 1.2% of specific emissions on average per year. When the regulation was announced in 2006, the average reduction rate increased to 2.4% per year. In 2009, the regulation was implemented, which increased the reduction rate to 3.2% on average. During the voluntary agreement phase, the relative reduction between 2000 and 2006 was 6.3%. The announcement and implementation of the policy led to a decrease of 24.2% between 2006 (161 gCO 2 /km) and 2019 (122 gCO 2 /km). Therefore, the regulation successfully impacts the average specific emissions of newly registered cars.

Specific CO 2 emissions from test cycle (formerly NEDC), the introduction of WLTP test cycle, and previous year change (own figure based on [ 35 ]) from 2000 to 2019. Blue line: Average specific CO 2 emissions of newly registered passenger cars

Overall, the targets of the CO 2 fleet limit can be summarized as follows:

Limitation of CO 2 emissions from newly registered vehicles (cars and LDV)

No detriment to low-volume car manufactures

In the following, the performance of key aspects for the selected criteria is summarized and broken down in Table 8 .

The targets were achieved, as the CO 2 fleet limits resulted in an overall reduction of 29.3% regarding the specific CO 2 emissions of newly registered cars. Furthermore, low-volume manufacturers were not harmed by this regulation.

According to Gibson et al. (2014), the CO 2 abatement costs of this regulation were estimated between 32.4 and 39.8 €/tonCO 2 . Although the abatement costs are hard to evaluate due to the development cost of manufacturers and the cost of developing and running the test cycles (with each car model), the regulation is assessed as cost-effective. Furthermore, the penalties for non-compliance are high.

Overall, the regulation is practically feasible as test cycles are standardized and reproducible. However, as laboratory test cycles on test facilities are used, they do not represent real-driving emissions (RDE). Therefore, RDE was added to WLTP. As RDE is much higher than the laboratory emissions, conformity factors were introduced.

Car labeling

Car energy labeling ensures that relevant information about the vehicle is provided to consumers, containing fuel economy and CO 2 emissions (classification from “A”—high to “G”—low) [ 26 , 49 ]. The car energy labels classification is based on the WLTP test cycles (formerly NEDC) results.

The “Trends of car purchase report 2021” provides selection criteria of surveyed customers, shown in Fig.  3 . The criteria are categorized into economic and socio-psychologic criteria. Price–performance ratio, consumption, and price are the main economic criteria for customers, while comfort, safety, and design are the leading socio-psychologic criteria. As environmental friendliness is in 8th place, it is less important for a purchase. Customers became more aware of emissions produced by their behavior during the last decade, but awareness and acceptance need to be further increased for less emitting cars [ 4 ]

Selection criteria of car purchase from trends of car purchase 2021, based on [ 4 ]

The targets of car labeling can be summarized as follows:

Incentives for consumers to buy cars that use less fuel [ 49 ].

Visualize carbon dioxide and fuel efficiency [ 43 ].

The Car Labeling Directive (Directive 1999/94/EC) of December 1999 is a demand-side directive that should support manufacturers in meeting specific CO 2 targets [ 26 ].

Labeling aims at environmental and behavioral economics by influencing customer's choices [ 14 , 48 ].

In the following, the performance of key aspects for the selected criteria is summarized and detailly shown in Table 9 .

Car labels enhanced customer awareness due to different studies. However, major selection criteria for car purchases are price–performance ratio and comfort (1st and 2nd), while environmental friendliness is in 8th place. While consumption is another main criterion (4th place, see Fig.  3 ), car labeling might become more impactful due to rising CO 2 taxes and a subsequent increase in fuel costs.

The results show that car labels are cost-effective in reaching customers, impacting purchase behavior, and creating awareness.

The practical feasibility of labels is also given, as they provide essential consumption and emissions information directly to the customer during the purchase process. Furthermore, car labels can help pave the way for a change to more environmental-friendly social norms.

Vehicle tax

Vehicle (ownership) tax sets common rules for the taxation of all kinds of motor vehicles. Therefore, the EU lays down general principles in the Treaties to which national provisions must adapt [ 23 , 31 ]. As the role of CO 2 emissions evolved, the taxation was adjusted several times during the last decades [ 75 ]. Furthermore, the calculation became more complicated as it differentiates between vehicle types and size criteria. Typical criteria are fuels (diesel, gasoline, alternative), type (car, commercial vehicles, motorcycles and more), emission standard (Euro Norm), registration year, CO 2 emissions (WLTP), and gross vehicle weight (commercial vehicles) [ 55 , 81 ]. This study exclusively evaluates the influence of vehicle taxation on passenger cars. The taxation system in 2021 and before for cars in Germany is shown in the Additional file 1 (Cars: SM 3, SM 4; HDV: SM 6).

The targets of the vehicle tax can be summarized as follows:

Create unrestricted state revenue for the state budget from private and commercial vehicle ownership [ 76 ].

Environmental steering effect on alternative and low-carbon powertrain technologies [ 76 ].

In the following, the performance of key aspects for the selected criteria is summarized and broken down in Table 10 .

The vehicle ownership tax achieved its tax revenue target due to a state income of 9.53 Bil. €. Regarding environmental effectiveness, the inclusion of CO 2 emissions based on the WLTP test cycle (formerly NEDC) added an internalization of external effects from those emissions. It changed the displacement and fuel type-based tax system to a hybrid system. Furthermore, the former linear tax system was developed into a staggering tax system based on CO 2 emissions to tax vehicles with higher emission values and thus heavier and larger vehicles more severely.

The vehicle tax system is practical and flexible, as it allowed the change to a combination of a displacement- and a CO 2 -based system. Furthermore, older emission standards are higher taxed as well as diesel cars, which motivates buyers to buy more fuel-efficient, low consumption and low emission vehicles.

Energy tax applies to all energy carriers. The annual tax revenue in Germany amounts to around 38 billion euros. In the transport sector, diesel and gasoline are the most relevant. The tax per liter is about 0.47 € for diesel and 0.67 € for gas, although the carbon- and thus the energy content of diesel is significantly higher [ 80 ]. The lower cost of diesel is because, in 2003, the EU adopted a directive to standardize oil prices. To reduce the distortion of competition between industries, a possibility of "special tax treatment" was introduced for diesel [ 37 ]. The energy tax is not further evaluated here, as it was not designed as an environmental steering instrument until 2020, which changed in 2021 with the revision of the energy tax directive [ 80 ].

The CO 2 tax is a quantity-based levy that applies to Germany's transport and heat sectors. The tax started in 2021 with a fixed price of 25€/ton and increased gradually to 55€/ton in 2025. In 2026, the tax will be transferred to a cap-and-trade system (NEHS), such as the EU ETS. In the long term, the plan is to transfer the national systems within the EU to the EU ETS [ 16 ]. Sweden and Switzerland already implemented CO 2 tax several years ago, but as their economies, population, GDP and land area are fully different, the results of this tax type are hard to project on Germany. In Germany, the CO 2 tax was recently implemented during the COVID-19 pandemic. The pandemic impacted fuel and energy consumption of the (road) transport sector heavily [ 74 ]. Therefore, reliable data is not available in 2022 to evaluate the impact on consumption behavior without other influences. Subsequent studies might provide further insights on the performance of the CO 2 tax on-road transportation in Germany.

Low emission zones

Low Emissions Zones (LEZ) are particularly aimed at curbing air pollution from road traffic. A LEZ is a geographical zone, usually in densely populated cities, into which only vehicles meeting a certain emission standard are allowed to enter [ 39 ]. First, it trailed in Sweden in 1996, LEZs were introduced in Germany in 2008. In 2021, 57 German cities had established LEZs [70, 78].

The targets of LEZs can be summarized as follows:

Limit air pollution to

a yearly average of 40 mg/m 3 , a daily average of 50 µg/m. 3 for particles bigger than 10 nm (> PM10).

a daily average may not exceed more than 35 days per calendar year.

yearly average NO x 40 µg/m 3 [ 70 ].

Pestel et al. (2019) investigated the effect of LEZ in Germany on the nearby hospitalization for respiratory diseases related to air pollution from traffic. The authors compared the number and severity of illnesses in hospital catchment areas before and after the introduction of LEZs. The study found that:

Hospitals with catchment areas located in an environmental zone (LEZ) diagnose significantly fewer air pollution-related diseases.

Air quality improved considerably by reducing NO 2 and PM10 concentrations [ 10 ].

Improvement of public health, mainly by reducing the incidence of chronic diseases of the circulatory and respiratory systems.

Traffic volumes and traffic-related diseases (stress, injuries) were not affected by environmental zones.

46 Bil. € for diseases of the circulatory system, making them the most expensive type of disease with 2.9 million cases.

Reductions in the incidence of diseases of the circulatory system may directly reduce society's healthcare costs [ 58 ].

Results from Euro standards 1–3, whether the diesel ban including Euro 4–6 would yield any further health improvement must be researched [ 58 ]. Analysis of data from 26 monitoring stations, after correction for changes measured at background stations and traffic stations outside the environmental zones, respectively, showed a decrease in pollution of 2.1 µg/m 3 and 2.4 µg/m 3 for fine particulate matter (PM10) and 3.7 and 1.2 µg/m 3 for NO 2 as an annual average.

Margaryan et al. (2021) found that LEZ led to a 3% decline of PM10, while NO x showed an insignificant reduction. The number of patients with cardiovascular disease declined by 2–3%, strongly for those aged above 65. Back-of-the-envelope cost–benefit analysis suggests health benefits of nearly 4.43 billion Euro that have come at the cost of 2. 3 billion Euro for vehicle upgrading [ 57 ]. Similar results were found in other studies [ 3 , 39 , 51 ].

In the following, the performance of key aspects for the selected criteria is shown in Table 11 .

Truck road toll

The Truck Toll is a distance-based road usage charge exclusively for heavy-duty vehicles in Germany. The toll was introduced in 2005 and represented a system change from tax-based to user-based financing of the national trunk road network. In 2020, the state income from the toll amounted to 7.4 Bil. € [ 8 ] Alternatively, the charge can be time-based (vignette), as applied in Austria.

The targets of the truck toll can be summarized as follows:

Shifting freight traffic to the railways (relief effects on the trunk roads, positive ecological effects, economic strengthening of the railways) [ 40 , 69 ].

A gradual toll reduction for electric and natural gas vehicles (Federal Highway Toll Act) [ 8 ].

Helping to reach CO 2 reduction targets in the transport sector [ 36 ].

A shift of investment costs from the state to the user (polluter-pays principle) [ 40 ].

In the following, the performance of key aspects for the selected criteria is summarized and detailly shown in Table 12 .

Set targets of the German truck toll are mostly achieved. While the total transport performance increased, the transport performance of road and rail transportation expanded as well. Road transportation increased by 28.2% and rail transport by only 17.4%, which led to a decrease in rail transport share and did not meet the goal of shifting from road to the more environmental-friendly rail (see Additional file 1 : SM 14 and SM 16). Implementation of a toll supports low-carbon technologies by freeing or partly charging those technologies. In other cities (e.g., Milan), a truck toll effectively achieved a CO 2 emissions reduction [ 12 ].

The truck toll applies the polluter-pays principle, successfully shifted road infrastructure investment costs, and reduced state expenditure by 80%. However, the toll collection costs account for 15% of the total revenue.

Except for the complex and cost-effective collection system implementation, the truck toll is practically feasible. Social and industrial acceptance is given due to the fact that this toll directly finances tax discounts and improved infrastructure. Furthermore, road tax reductions for low-carbon trucks help speed up transitions, reduce vehicle trips and rebound effects.

Final consideration

This study evaluated applied environmental policy instruments in the German road transportation sector until 2021. A wide range of instruments was applied to enhance air quality, reduce fuel consumption, and mitigate emissions. Findings demonstrate that non-market-based instruments constitute the preferred application for producers and distributors, whereas market-based instruments are chosen for consumer regulation. Furthermore, the study found:

The effectiveness of single instruments is hard to assess and separate from other instruments, as some are aiming for the same goals

Efficiency measures—partly induced by direct regulations—toward more efficient engines and vehicles can have a rebound effect, leading to more demand, traffic and emissions

Measures to shift the mode of transport were not pursued consistently—the number of vehicles and transport performance steadily increased

Although governments are setting the regulations, the influence of regulators on the transportation sector is somewhat limited. A regulator cannot control the total transport development. Therefore, specific emissions can be reduced by implementing measures (e.g., fleet limits), but economic growth or side- and rebound effects might lead to higher transportation performance (e.g., more driven kilometers, traffic volume), nullifying the improvement from an emissions perspective. Other underlying reasons are changes in purchase power and processes of social change.

Some of the criteria can only be applied to the respective instruments to a limited extent. For example, the focus on CO2 effectiveness shows that some instruments do not have a direct CO2 reduction target but have an indirect effect, such as the Euro standard.

Furthermore, the quantitative efficiency of Euro norms and vehicle tax cannot be evaluated due to a lack of research.

Qualitative assessment of efficiency is also difficult, as the costs and benefits are incurred in different places that are not directly offset against each other. This study prefers the macroeconomic view of costs to the microeconomic perspective.

While microeconomic costs are relevant for companies and individuals to implement a policy, macroeconomic costs are crucial for using mechanisms and instruments. Since EPIs pursue societal goals, such as environmental protection, CO2 reduction, and air quality, the countervailing macroeconomic costs must be considered first.

Even though the transport sector has not shown a reduction in emissions, the impact of emission-increasing developments—such as higher transport capacities, heavier vehicles and more power—has been mitigated through introduced policies.

Non-market-based instruments

The results identify several characteristics of non-market-based instruments. Performance standards and technology mandates are popular direct regulations in the road transport sector (FQD, Euro Norm, CO 2 fleet limit, directive on mobile air conditioning systems).

Direct regulation is difficult to implement but can take effect very quickly and is helpful for urgent problems. Thus, direct regulation has been used in the Montreal Protocol for CFC Mitigation or emissions standards (Euro Norms). However, NMBIs are very static by nature, as they are designed for specific circumstances and, therefore, do not adapt well to change (e.g., technological innovations). Moreover, they require the regulator in charge to reasonably predict technical and environmental conditions. Regulatory measures often have legislative gaps and are very complex when it comes to covering a large field. This property makes these measures—once established—resistant to innovation for alternative problem-solving.

A regulatory measure initially appears as a simple solution to a problem, but it entails a series of significant issues, revisions and adjustments. However, the effectiveness of direct interventions is undisputed. Still, economic efficiency suffers from the intervention and severely limits the ability of the regulated group to act, thus reducing the potential for alternative and innovative solutions.

The market often relies on government assistance, as seen in the transportation transition. Direct interventions—such as technology mandates—can be critical in planning government investments in necessary infrastructure. For example, the development of a charging station infrastructure, which precedes the (indirect) technology mandate of battery–electric mobility, stands in competition with developing a hydrogen refueling station network from an investment perspective. However, as governments face a limited budget and both paths call for intense investment, the regulator must decide. This decision, however, significantly limits producers’ and customers’ choices.

Market-based instruments

Market-based instruments, especially emissions trading, represent economically more efficient approaches and do not restrict the range of solutions to the same extent. However, a fundamental problem of economic evaluation arises here:

The market and its imperfections:

As markets only cover a specific area of economic and private behavior and interactions with nature, an incomplete representation of reality arises. Thus, external effects which have not yet been internalized—i.e., not monetized and thus not appearing in the market system—do not affect the decision-making of market participants.

History has shown that many relevant external effects on the economy and environment are not internalized in market systems. For example, social costs created from respiratory diseases related to air pollution from fossil fuel burning in power plants and the mobility sector are not represented in the fuels’ cost structure. Other consequences are decreased quality of life, lower life expectancy, or loss of workforce due to induced illness by air pollution. As those effects are hard to internalize, governments decided to directly regulate air pollution (Euro standard and low emission zones). Furthermore, transport performance is not related to its eco-efficiency, which means high-emission vehicles can have the same transport performance but cause more air pollution. These costs only occur partly as higher fuel costs. The consumers’ perspective only shows higher fuel consumption and, therefore, higher transport costs, but these do not represent the actual occurring costs. As a result, many effects occur as social costs and remain public.

External effects, which affect markets, are called missing markets. Therefore, the scopes of the existing markets must be expanded to provide a sufficient representation of actual behavior and its effects (currently occurring as externalities and social costs).

An example of this is the ban on internal combustion vehicles. This mandate is an inefficient solution from an internalized market perspective as there are cheaper ways to reduce CO 2 (low-hanging fruit principle). However, harmful emissions to health are not priced in existing cost structures yet. Therefore, the occurring costs on the health system are not represented in this price, leading to an insufficient representation. Consequences are the reduced quality of life, costs of treatments and lost work due to respiratory illnesses. Insufficient internalization of costs can be found in various energy policy examples in Germany (coal mining in the Ruhr area, nuclear power).

How much of the present can the future take?

The economic view causes tension to arise between cost-efficiency indicators and generational fairness. There is a dilemma between static and dynamic efficiency: On the one hand, static efficiency is most relevant to align with the current economic situation and represents economic values like the return of investment or liquidity. Due to market imperfections, even static efficiency is not representing the actual costs of behaviors and might lead to a wrong path from a long-term perspective. Static efficiency does not include social costs, as those occur in the future. On the other hand, dynamic efficiency can represent these effects, but it is hard to predict, as future costs are highly uncertain. Static and dynamic efficiency can be weighed against each other using the interest rate and inflation. Thus, the return on investment of static efficiency is lower from the perspective of (future) dynamic efficiency. Furthermore, cost-efficiency indicators are different from micro and macroeconomic perspectives, leading to tension between governments and market participants.

Internalization of climate change—CO 2 as a determinant

Internalizing external effects is crucial for a realistic—or at least sufficient—market representation of behaviors and economic actions. Regarding the market representation of climate change effects, CO 2 is ideally suited as an evaluation system for a spectrum of external effects. The concept of the global warming potential can convert the climate impact of different (atmospheric) gases into a single unit—CO 2eq —which is reflected in the market via a price, thus combining these systems. However, the internalization of CO 2 has some limitations:

The instrument mainly focuses on emissions from fossil fuels. A complete representation of emissions will require a holistic assessment, such as the cradle-to-grave approach.

Time preference: Time preference describes how a resource used today is valued compared to the ability to use the same resource in the future. Assuming that the CO 2 budget is limited until 2050 or the end of the century, CO 2 becomes a resource that can only be "used up" to a certain extent. How the budget is used up over time presents the dilemma between current and future consumption.

The pricing scheme in emission trading systems still does not reflect emissions' time preference, as CO2 pricing in emissions trading is statically formed.

The comparison of current and future use of resources can be represented in a CO 2 interest rate. Thus, it can be argued that CO2 valuation shows a similar behavior with similar problems as a currency.

Therefore, it can be argued that CO2 as the currency of our climate—when it is sufficiently developed and other externalities are internalized—could be the market-side representation of human interaction with nature.

As long as a market system does not reflect all—or at least the most relevant—external effects of economic trade, a purely market-oriented approach via emissions trading are neither effective nor sufficiently reflects reality.

Therefore, internalizing other relevant external effects is desirable and leads to more market-oriented approaches, openness to solutions, and a reduced necessity for direct regulations. A realistic monetary representation is complicated in some cases (quality of life, happiness, health). Whether all relevant external effects can be realistically mapped on the market side at all borders remains a technical, if not a philosophical question. In conclusion, with regard to the study results, it can be claimed that there seems to be a historical development from direct regulations to a more market-oriented search for solutions using market mechanisms, such as competition and efficiency enhancement. Integration of CO 2 into the market system allows comparing emissions to other cost factors, with the CO 2 price reflecting the weighting of this cost factor in the overall bill.

Even though the actual costs of today's emissions are not yet fully reflected, the market systems are improving by internalization, and it is important to encourage this development. Nevertheless, the support of the market system by regulatory measures will continue to be necessary, mainly if market actions cause external effects that are hard to monetize.

We recommend studying and assessing the transport policy after 2021, with a special focus on heavy-duty transportation, EURO 7, and alternative powertrains/fuels. Furthermore, we recommend taking life-cycle assessment into account for comparison of different powertrains and not only focusing on operative emissions.

Availability of data and materials

All data generated or analyzed during this study are included in this published article (and its Additional files).

Abbreviations

Active (Green) Technology Support

Command-and-control

Chlorofluorocarbon

Climate monitoring mechanism

Compressed natural gas

Carbon-dioxide

Exempli Gratia (for example)

Energy efficiency directive

EGR exhaust gas recirculation

• Environmental policy instruments

Emissions trading scheme

European Union

European Union’s Emissions Trading Scheme

Electric vehicle

Fuel quality directive

Greenhouse gases

International Council of Clean Transportation

Intergovernmental Panel on Climate Change

Light-emitting diode

Leadership in Energy and Environmental Design

Low-emission zone

New European drive cycle

No observed adverse effect level

Nitrogen-oxide

On board diagnostics

Particle matter

Research, development and deployment

Real driving emissions

Renewable energy directive

Return of investment

Selective catalytic reduction

Supplementary material

Sport utility vehicle

Terawatt hours

United Nations

United Nations framework convention on climate change

Vehicle energy consumption calculation tool

Worldwide harmonized light vehicles test procedure

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European road transport policy assessment: a case study for Germany.

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Schulthoff, M., Kaltschmitt, M., Balzer, C. et al. European road transport policy assessment: a case study for Germany. Environ Sci Eur 34 , 92 (2022). https://doi.org/10.1186/s12302-022-00663-7

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Home » Management Case Studies » Case Study: Wal-Mart’s Failure in Germany

Case Study: Wal-Mart’s Failure in Germany

Wal-Mart Stores, Inc. is the largest retailer in the world, the world’s second-largest company and the nation’s largest non-governmental employer. Wal-Mart Stores, Inc. operates retail stores in various retailing formats in all 50 states in the United States. The Company’s mass merchandising operations serve its customers primarily through the operation of three segments. The Wal-Mart Stores segment includes its discount stores, Supercenters, and Neighborhood Markets in the United States. The Sam’s club segment includes the warehouse membership clubs in the United States. The Company’s subsidiary, McLane Company, Inc. provides products and distribution services to retail industry and institutional foodservice customers. Wal-Mart serves customers and members more than 200 million times per week at more than 8,416 retail units under 53 different banners in 15 countries. With fiscal year 2010 sales of $405 billion, Wal-Mart employs more than 2.1 million associates worldwide. Nearly 75% of its stores are in the United States, but Wal-Mart is expanding internationally. The Group is engaged in the operations of retail stores located in all 50 states of the United States, Argentina, Brazil, Canada, Japan, Puerto Rico and the United Kingdom, Central America, Chile, Mexico,India and China.

Wal-Mart’s Entry and Operation in Germany

Wal-Mart’s initial entry into German market was through the acquisitions of renowned 21 store Wertkauf chain for an estimated $1.04 billion in December 1997.It was  followed one year later by the acquisition of In-terspar’s 74 hypermarkets from Spar Handels AG, the German unit of the French Intermarche Group , for ‚¬560 million. Thus Wal-Mart immediately became the country’s fourth biggest operator of hypermarkets. However, with a turnover of around ‚¬2.9 billion, and a stagnating market share of just 1.1 per cent, the US giant still was a negligible one in the German retail market. Even worse, with estimated accumulated losses of more than ‚¬ 1 billion, it is literally drowning in red ink although, according to Wal-Mart Germany’s CEO, Kay Hafner, its non food assortment, which accounts for around 50 per cent of its revenues, is profitable. Instead of expanding its network of stores by 50 units by early 2001, as originally planned, the company has been forced to close two big outlets, while at the same time it was only able to fully remodel three locations into its flagship Super center format. Due to its problems the company also had to lay off around 1.000 staff. On July 2006, Wal-Mart announced   its official defeat in Germany and  would sell its 85 German stores to the rival supermarket chain Metro and would book a pre-tax loss of about $1 billion ( £536 million) on the failed venture.

A Critical Analysis of Reasons for Wal-Mart’s Failure in Germany

There were several factors that contributed to Wal-Mart’s Failure in Germany. Amazing management blunders have plagued Wal-Mart’s German operation from the very start. Wal-Mart’s major mistakes on the German market may be summarized as follows.

• Cultural Insensitivity was the major reason of failure.
• Entry to German market by acquisition strategy.
• Failure to deliver on its legendary “every-day low prices” and “excellent service” value   proposition.
• Bad Publicity about the company due to breaking of some prevailing German law and regulations.

In January 1997, Wal-Mart had first entry in Europe market with the acquisition of Wertkauf hypermarkets in Germany.   Later in that year, Wal-Mart also acquired Interspar, another German hypermarket chain. While its first move — the 1997 takeover of the 21 Wertkaufstores  was indeed a shrewd one, given that company’s excellent earnings, its competitive locations, and its very capable management. Wal-Mart’s 1998 follow-updeal with Spar for 74 hypermarkets was widely judged an ill-informed, ill-advised act, for several reasons. Spar is considered to be the weakest player on the German market due to its mostly run-down stores, very heterogeneous in size and format, with the majority of them located in less well-off inner-city residential areas.

Wal-Mart’s cultural insensitivity led to its failure in Germany.

Wal-Mart’s Failure in Germany – A Case of Cultural Insensitivity

Most of the Global mergers and acquisitions failed to produce any benefit for the shareholders or reduced value, which was mainly due to the lack of intercultural competence. Lack of sensitivity and understanding of language barriers, local traditions, consumer behavior, merchandising, and employment practices irreversibly damaged Wal-Mart’s image in Germany. One of the main reasons that failed Wal-Mart in Germany is when it attempted to transport the company’s unique culture and retailing concept to the new country. The top management refused to even acknowledge the differences in customer behavior and culture in Germany when compared to its US customers, and the top management failed to listen to the feedback from its employees. Not every new cross- border retailer can be a retail giant outer its home.   The mistake of exporting its culture wholesale, rather than adapting to local market, leads Wal-Mart failed in Germany market.

Wal-Mart’s ambitions to position itself profitably in European markets through Germany have been hit badly by their inability to fully understand and to adapt to the specific conditions of doing business in other countries. This exposed their obvious lack of intercultural competence and management skills. The main challenge of post-merger integration is further complicated significantly if it is in a Cross-border Merger or acquisition, with all issues frequently being compounded by a lack of language and culture bridging skills. Failure to accomplish this task satisfactorily, results in mutual distrust, de-motivation and negatively impacts the merged companies’ competitiveness, profits and shareholder value. This is exactly what happened to Wal-Mart Germany.

Following are the main two factors that contributed to the  Wal-Mart’s Failure in Germany;

1) Specific Difference in German Consumer behavior and Culture in comparison with US consumers:

The biggest mistake of Wal-Mart was to ignore the local culture, local buying habits and impose an American boss on its German operations. Wal-Mart stores are designed for customers who are willing to spend lot of time shopping. But in Germany, the shopping hours are shorter: Shops close by 5 PM on weekdays, and no shopping on Sundays. This meant that customers don’t have the habit of spending lots of time in a store – wandering around for the things they need. Coupled with this problem, German customers do not like to be assisted by Wal-Mart’s friendly store assistants. Germans prefer to do their own search for bargains. Instead of understanding and adjusting to the culture of its clients, Wal-Mart tried to impose their Culture on to the Customers, which never worked out.

Germans like to see the advertised discount products upfront without having to ask the store assistant. This implies that the discount products must be placed at the eye level. Instead Wal-Mart chose to use its US style merchandise display strategy – where premium priced products are kept at eye level and discount products are kept at higher shelf or in the bottom racks. This irritated the German shoppers. Wal-Mart also got its store inventory wrong, Wal-Mart stocked its store with clothes, hardware, electronics and other non-food products were given much bigger floor space than food products, as a result more than 50% of the revenue was from non-food products. But other German retailers stock more of food products. For example for Metro, food products constitute more than 75% of the revenue. Germans prefer to bag groceries themselves into reusable carriers, or at least to pay a small fee for the avoidable sin of needing a plastic bag.

German’s are introvert in nature and doesn’t like display of emotion in public, as they always care for their private personal space. Employees, like the reserved customers, didn’t care for Wal-Mart’s public displays of corporate moral such as the morning cheer. The German Customer’s even didn’t liked to be accompanied by the Cheerful employees either, as they would like to make choices by themselves. These are cultural misunderstandings as well, but one could say the cultural philosophy of Wal-Mart could not survive in the context of a German culture with a Happy Planet Index significantly higher than America’s.

2) Inefficient Top Management which ignored the relevance of local Culture:

It was clear that the cultural insensitivity of Wal-Mart started right at the top management. To begin with, it appointed four CEOs during its first four years of operation. The first head of German operations was Rob Tiarks, an expat from the USA – who did not understand Germany or its culture. He had previously supervised around 200 Supercenters in America. Not only did he not speak any German. Due to his unwillingness to learn the language, English was soon decreed as the official company language at the management level. He also ignores the complexities and the legal framework of the German retail market, ignoring any strategic advice presented to him by former Wertkauf executives. This has resulted in the resignation of top three management executives from Wertkauf. His successors were also unsuccessful in integrating German Outlets with the Wal-Mart’s Business model and culture.

Other Reasons of Failure

A number of factors that resulted Wal-Mart’s failure in Germany are such as different corporate culture, political influence, stiff competition and inefficient management and marketing strategies . Firstly, David Wild, Wal-Mart’s CEO in 2004, believed that cultural differences between American and German consumers were considerable challenges to Wal-Mart. Debby, CEO in 2006, concluded that German shoppers are accustomed to shop at small scale discount stores such as Aldi and Netto that provides a limited range of products with special offers each week and no customer service, unlike US customers. In addition to different corporate culture, the competition has become gradually more intense between Wal-Mart and domestic retailers. The price difference has so lessened that sometimes even Wal-Mart had a higher price than their competitors. Consequently, consumers had little incentive to visit Wal-Mart Germany because of no obvious price advantage.

Some other factors that lead to Wal-Mart’s failure in Germany were, their strategy of acquiring the top competitor did not work, as the German government did its best to ensure the welfare of the domestic players. Also, due to wage restrictions, Wal-Mart could not practice wage bargaining, as it did back in U.S, this was a huge, uncommon expenditure for the company. Its American strategy of restricting employee freedom and forcing them to work extra hours, brought up problems of high labour turnover and a negative image as an employer. Wal-Mart failed to have an effective management at the top level. It’s CEO’s changed every year, this in an obvious way effected the company’s performance. Wal-Mart constantly ignored the strictness of German laws, and was charged heavy penalties for doing so. One of the most challenging thing for Wal-Mart was capturing the market- share. As per German legislation it was illegal to sell products below cost,because of which Wal-Mart could never achieve the ‘Low price leader’ tag.

It is impossible to smoothly run any organization, until there is co-operation between the employees and the employer. Wal-Mart faced a severe labour unrest,which hampered its brand-image. Kay Hafner,CEO of Wal-Mart reduced the wages to cut cost, this negatively influenced individual behaviour , as an anti-union decision. As suggested by Arndt and Knorr, a firm needs to understand the specifications when indulging in global expansion.Out of all the CEO’s, only David Wild has been sensitive to cultural difference.He did bring about changes based on this understanding,which had some positive results,yet not profitable enough to impress investors for future investments.

Moreover,as per German legislation their were some specific retail related laws, such as, limited legal working hours (80 hours/week) which were way less than the other European countries and had strict rules governing closure on Sunday’s and holidays. Wal-Mart repeatedly infringement German laws but were able to do away with it mainly because of global presence and influence on the government of US which played a major role in global politics. Some of incidences where the company broke few laws and was able to get away are summed up below:

• ‘Unfair trade’ practices such as selling goods below the cost price was prohibited in Germany but Wal-Mart was found violating these laws as it randomly sold some product below cost.
• German law required a company to disclose it financial statements annually, Wal-Mart seldom did that and was spared without any fine or legal proceedings at number of occasions.
• Obligatory Deposit Regulation’s law stipulated the retailer to provide deposit-refund-system on few products like metal beverages, cans etc. But Wal-Mart never followed this law.

Thus from the above incidences it can be concluded that Wal-Mart used its global influence to refrain from some of the German laws.

However, because German culture is quite different from American culture and because of unfamiliarity with the legislation, it would be difficult for Wal-Mart to make marketing and promotion right. And in fact these difficulties had been proved in Wal-Mart Germany. Consequently, rather than choosing Germany as the gateway to Europe, virtually after two years of operating in Germany it had entered in U.K. Even though U.K is not in the Euro zone and its geographic location is less favorable than Germany, it has a similar culture and legal environment as U.S. which makes it easier to operate the company’s business and strategies. It has considerable success in the UK market which is called by as a ‘Wal-Mart-ready’ market. Therefore, the lessons learned from from Wal-Mart’s failure in Germany has proven useful for U.K.

Suggestions and Recommendations

Cross-border, Cross-cultural business is a challenge even for the biggest companies. Companies have to be sensitive to the local cultures and tailor their offerings to local market. To localize their offerings, Wal-Mart and other Companies that are going global companies must carry out cultural assessment of the Citizens of the Country before acquisitions. All their Corporate Business and Communication strategies should be based on this cultural assessment. This will help companies measure the effectiveness of its localization efforts and make adequate changes in local strategy & tactics as and when required. Considering the following steps would help Wal-Mart or any other Company while they are on lookout of Global alliance or business.

1. Political, Social, Economic and Cultural Analysis of the Country

Before expanding its business operations to a new country, the Company should understand the Political, Social, Economic and cultural aspects of the Country in depth. Wal-Mart’s case, Germany was selected primarily because of a central European location and economic attractiveness of the Wertkauf acquisition. But a serious research would have shown that Germany had strong national values resistant to change ; possibly the most deeply rooted retail traditions in Western Europe. This could have avoided either Wal-Mart’s selection of the Country or the strategies it has adopted in Germany.

2. Go global and think they are local

After conducting an in depth research about the prevailing trends in the customer’s Country, the Company should be ready to modify its own identity to suit itself to the cultural differences without compromising much on its Corporate Mission . This step will also force organizations to clearly define globalization goals. Wal-Mart put the company name on many German stores before being fully established. Immediately, the run down stores left an impression on consumers who formed a negative image of the Wal-Mart name.

3. Employment of Cross-Cultural Management approaches

Employment of Hofsted’s Culture Dimensions or HT&T Analysis will help Companies in understanding the minute cultural differences between the countries. For example , Communitarianism over Individualism

Germans degree of communitarianism is on the higher side mainly because Germans prefer participating on a team. Most Germans see business as a group of related persons working together. But, most of Americans see their company as a set of functions, tasks, people, machines and payments in which individuals compete.

This difference in Cultural dimensions between the 2 countries has resulted in inside management conflict among the employees, which also resulted in resignation of efficient German executives from Wal-Mart post integration.

Understanding the cultural dimensions of a Country through proven Cross-Culture models will always help a company to formulate a specific approach that will encourage team spirit and joy among the Global Team.

4. Continuous Updation of Strategies to successfully withstand the local competition

It is very important for a Global firm to continuously analyse the impact of their various strategies on the local market. Understand the shortfalls, and modify it in such a way as to cater the local market in a much better way than the competitors. It is always better to scrutinize the strategies adopted by them with a panel of local experts, as they will be having a better picture about the local consuming behavior and culture. Perceptions do matter a lot, So a surveys to find the customer’s perception about the company will also help them to change their strategies accordingly.

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Country Case Study Germany

The German case study will zoom into a specific micro scene of AI and data analytics in public administration. The research will focus on studying the practices of implementing such systems in the context of migration politics and focus on changed practices for integrating refugees into the German social system.

Studying practices implies a detailed observation of human activities and routines in the interaction with the AI system but also whether and how the AI system impacts on human actors, that is studying the relationships within situations of AI-based local governance taking into account all involved factors. The objective of the research is studying in microscopic detail the change of practices through AI-based social assessment.

Netzpolitik.org https://netzpolitik.org/2019/asylbehoerde-sucht-mit-kuenstlicher-intelligenz-nach-auffaelligen-gefluechteten/

At the German Federal Office for Migration and Refugees (Bundesamt für Migration und Flüchtlinge BAMF) AI methods are being applied in three projects: In the ZPE project (central incoming mail) as well as in the EGVP projects (electronic court and administrative mailbox) and in the “profile analysis” project. The decision will be made depending on the possible applications and the specifications for the IT architecture. Since these are new methods, external expertise is useful. It will be involved as the need arises. BAMF sees the greatest potential in all processes in which large amounts of data are processed and employees only ever process a small section, so that connections and patterns are difficult to identify.

In 2019, the Bundestag’s Enquete Commission on Artificial Intelligence asked for a more detailed explanation of the AI projects of German authorities and ministries. The NGO Netzpolitik published the answers of the Ministry of the Interior on AI in the BAMF.

The BAMF is currently running a pilot project on “profile analysis”. According to the group leader for processes and IT, the profile analysis was developed “in order to be able to fulfil the BAMF’s legal reporting obligations to security authorities more easily and quickly”.

For example, the migration authority reports persons to the Office for the Protection of the Constitution if information from hearings suggests that applicants could be of interest to the intelligence service. This is the case, for example, if there could be links to terrorism – be it as a victim or suspected perpetrator. The BAMF is increasingly forwarding data to the Federal Office for the Protection of the Constitution: In 2015, there were just over 500 cases, two years later the the Federal Office for the Protection of the Constitution (Verfassungsschutz) receives over 10,000 tips from the BAMF. It is not known how many actually relevant findings this brings.

At present, the software may only screen foreigners according to “security information”, but the purpose of such systems can easily be expanded. Technically, it would be easy to have computers pre-sort the applicants’ “prospects of staying”.

The BAMF has even more AI Systems in use. The Ministry of the Interior does not list all the AI technologies that the BAMF uses: The BAMF uses software to recognise dialects and gain clues as to where refugees come from. Even though this is scientifically controversial and the BAMF itself assumes a 15 percent error rate.

https://netzpolitik.org/2020/automatisiertes-misstrauen/

From January to November 2019, the BAMF used the dialect recognition software with almost 4,000 people. More than a quarter of them (1,056) came from Syria, followed by Algerians and refugees from Morocco and Sudan. This is the result of the answer given by the Federal Ministry of the Interior (BMI) to a question posed by Ulla Jelpke, a left-wing member of the Bundestag.

In 64 per cent of the tests, there is no information on whether the results of the dialect analysis support the statements of the persons concerned, and in 4.7 per cent of the cases there were contradictions, according to the BMI. In the same period last year, the software had been used somewhat more frequently: From September 2017 to mid-November 2018, it was used 6,284 times, but the number of asylum applications was also higher during this period.

Whether the procedure is suitable for obtaining reliable information on the origin of refugees is in doubt. On the one hand, researchers note that language cannot be clearly defined by national borders and can differ and change depending on a person’s life course and socialisation. For another, according to the BAMF, the software currently has an error rate of about 15 per cent. In these cases, it is up to the decision-makers to recognise these errors so that unjustified doubts about the applicant’s information are not included in asylum decisions.

Actually, the BAMF wanted to have the use of its language analysis software scientifically monitored as early as 2018. To date, nothing has happened. In response to a press enquiry, the Federal Office replied at the end of 2019: “Scientific monitoring is not currently taking place, but is planned for the future. In the meantime, there is no longer any mention of a specific time period. Meanwhile, the programme has been running for more than two years and has been used in the asylum procedures of more than ten thousand people.

Another AI system analyses text messages on the smartphones of those seeking protection – this also involves the language used by the applicant.

BAMF sees the greatest risks in the sole decision-making authority of machines, so that at BAMF only assistance systems are being developed that support employees, but will never have the final decision-making authority. On the other hand, the decision-makers and other employees are under a great deal of pressure, which tempts them to believe too much in the supposedly neutral results of the algorithms. This has already happened. Asylum seekers were accused of providing false information and their asylum applications were initially rejected.

The objectives of the German case study are to investigate the AI systems in use for deciding on migrants and asylum seekers in Schleswig-Holstein and Hamburg, two federal states in Northern Germany. For this purpose, the case study will utilize the common AI-FORA research approach of building a team of technical and social science partners for the case studies.

The research endeavor will integrate social sciences and technical sciences, including the following elements:

• Desk research to get an overview of the historical development of the case since the decision to use AI and the first implementation of AI systems. Targeted desk research will be facilitated by our cooperation partner “kommunit IT-Zweckverband” (https://www.kommunit.de), a company that develops and supports public administration software, especially for cities and municipalities in Schleswig-Holstein. This research will be done by the social science partner and serve as an input for the technical partner.
• In order to investigate the implementation and potential biases along the algorithmic value chain, the technical partners will investigate the functionality and decision-making processes of the software by means such as black-box analysis (Diakopoulos 2014). Subsequently, methods of Explainable AI (XAI) will be used to evaluate the impact of specific input values (features) on the software’s decisions and predictions. These insights will be used as an input for social science research that investigates the impact of the software on administrative procedures and the guiding social values.
• Participatory multi-stakeholder workshops organized by the social science partner will be utilizing specific access to migrants and asylum seekers through a current refugee project of the German Intermediary. In 2015, the Intermediary had received the Prize of the Refugee Council (Landesflüchtlingsrat Schleswig-Holstein ) for outstanding commitment to refugee aid.
• Workshops will bring together representatives from the federal government and public administration in Schleswig-Holstein, Schleswig-Holstein Refugee Council, representatives of the BAMF, NGOs such as Algorithmwatch, Netzpolitik but also charity initiatives supporting the refugee, and research and media that is dealing with the case to collect data on values, perspectives, opinions and attitudes of decision makers and agenda setters. These workshops will identify actors and possible modes of action in the cases. This provides the input for developing scenario simulations.
• Focus-group discussions will be undertaken with affected persons, i.e. (accepted and rejected) migrants and asylum seekers, and other BAMF clients. As the AI systems are meanwhile implemented for several years, the focus groups will enable to reveal if and how the practice of migration politics has changed and how this affects the clients of the process.
• Interviews with domain experts will help to understand the context dependencies of actors within these areas. Interviews will be undertaken with local decision makers, external experts in AI-based migration politics such as members of the Enquete Commission AI, as well as practitioners in the administration such as case managers and administration of the agencies. This will support the research in understanding the social, economic, political and administrative pressures that drive the debate around the case. This research will be undertaken by the social science partner.
• Since the media discourse strongly influences public opinion and sets agendas for public discourse, it will be analyzed which topics, arguments, and sentiments shape the media discourse in order to capture the lines of arguments. A first overview of the media discourse seems to indicate that the topic is discussed under the issues of bad project management, overspending money, and complaints by the case managers working with the system, but not so much under topics specifically to AI. This research will be undertaken by the social science partner.
• Multi-stakeholder workshops and interviews with case managers undertaken by the social science partner will support the technical science partner in identifying issues of relevance for the development of software in a co-creation lab that takes into account the values and interests of the participating stakeholders.

Own research of case study partners (previous projects, publications etc.) on the chosen domain:

These projects are relevant as they investigated responsible innovation especially involving civil society organisations and the implementation and development of AI technology.

• 2013-2016 EU project ProGReSS: PROmoting Global Responsible research & Social and Scientific innovation (FP7, Science in Society), Petra Ahrweiler (Principal Investigator), 350.000 EUR.
• 2013-2016 EU-Project GREAT: Governance for Responsible Innovation (FP7, Science in Society), Petra Ahrweiler (Principal Investigator), 350.000 EUR.
• BMBF DIGISTA – Materiality and Meaning of Urban Communication Practices – Urban Places as Location of Communication: The Example of Augsburg; Duration: 9/2018 – 8/2021; Connection to AI FORA: Future communication practices based on VR/AR; Sponsor and budget (for Elisabeth André): German Federal Ministry of Education and Research (BMBF), André’s team: ca. 245.000 €, Overall funding: 924.000 €
• BMBF EMPAT – Empathic Training Companions for Job Interviews; Duration: 3/2015 – 2/2018; Connection to AI FORA: Computer-based Assessment of People; Sponsor and budget: German Federal Ministry of Education and Research (BMBF), Elisabeth André’s team: ca. 240.000 €, overall funding: ca. 1,5 Mio € (without contribution by industries)
• DFG Research Group OC-Trust – Design of User Interfaces for trustworthy Organic Computing Systems; Duration: 10/2009 – 9/2015; Connection to AI FORA: Trustworthiness of AI Systems; Sponsor and budget (for E. André): German Science Foundation (DFG), 1 researcher, student researchers and travel over a period of six years

Some relevant publications for the case study:

• Yue Zhang, Andrea Michi, Johannes Wagner, Elisabeth André, Björn W. Schuller, Felix Weninger: A Generic Human-Machine Annotation Framework Based on Dynamic Cooperative Learning. IEEE Trans. Cybern. 50(3): 1230-1239 (2020)
• Kaska Porayska-Pomsta, Paola Rizzo, Ionut Damian, Tobias Baur, Elisabeth André, Nicolas Sabouret, Hazaël Jones, Keith Anderson, Evi Chryssafidou: Who’s Afraid of Job Interviews? Definitely a Question for User Modelling. UMAP 2014: 411-422
• Ahrweiler, P., Gilbert, N., Schrempf, B., Grimpe, B. and Jirotka, M. (2019) “The role of civil society organisations in European responsible research and innovation,” Journal of Responsible Innovation , 6(1), pp. 25–49. doi: 10.1080/23299460.2018.1534508.
• Ahrweiler, P. (2017): Simulationsexperimente realexperimenteller Politik – der Gewinn der Zukunftsdimension im Computerlabor. In: Boeschen, S., Gross, M. and W. Krohn (eds.): Experimentelle Gesellschaft. Baden-Baden, 199-237. (Simulation Experiments of Real-World Experimental Policy – Gaining the Dimension of the Future in the computational Laboratory).

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Super RTL wants to take over Nickelodeon in Germany

April 23, 2024 10.44 Europe/London By Jörn Krieger

The companies confirmed a corresponding agreement to German industry publication DWDL .

According to the agreement, Super RTL will take over Nickelodeon’s established satellite frequency on Astra (19.2° East) and operate the channel under its own Toggo brand after a transitional period, while continuing to show popular Nickelodeon series such as Paw Patrol and SpongeBob SquarePants . The Nickelodeon channel brand will disappear from German free-TV. Paramount intends to focus entirely on its streaming services Paramount+ and Pluto TV in the children’s sector in Germany in future.

“By expanding the partnership with Nickelodeon and Paramount, Super RTL is offering its young audience an even richer wealth of high-quality children’s programmes. The takeover of the satellite frequency creates a new valuable Toggo touchpoint for the target group of 3 to 13-year-olds. This fits perfectly into the strategy of making the Toggo brand present and tangible for children across as many channels and platforms as possible,” said Thorsten Braun, Head of Marketing, Brand & Consumer Products Officer at RTL Deutschland.

Michael Keidel, Vice President Ad Sales, Affiliate & Streaming Partnerships in Northern, Central & Eastern Europe at Paramount, added: “Thanks to the strategic partnership with Super RTL, the popular Nickelodeon characters and stories for children in Germany continue to have a firm place on free-TV. This fits perfectly into our strategy of entertaining fans via numerous high-reach touchpoints. These include our premium streaming service Paramount+, our free, ad-financed streaming service Pluto TV and our pay-TV channels.”

The transaction is subject to approval by the German Federal Cartel Office.

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About Jörn Krieger

Jörn reports on the latest developments in Germany, Austria and Switzerland. Since 1992, he has been working as a freelance journalist, specialised in digital media, broadcast technology, convergence and new markets. He also takes up University lectureships, writes articles in specialist publications, and produces radio reports. Jörn is also a moderator of panel discussions at industry events such as ANGA COM, Medientage München and IFA Berlin.

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Global projections of macroeconomic climate-change damages typically consider impacts from average annual and national temperatures over long time horizons 1 , 2 , 3 , 4 , 5 , 6 . Here we use recent empirical findings from more than 1,600 regions worldwide over the past 40 years to project sub-national damages from temperature and precipitation, including daily variability and extremes 7 , 8 . Using an empirical approach that provides a robust lower bound on the persistence of impacts on economic growth, we find that the world economy is committed to an income reduction of 19% within the next 26 years independent of future emission choices (relative to a baseline without climate impacts, likely range of 11–29% accounting for physical climate and empirical uncertainty). These damages already outweigh the mitigation costs required to limit global warming to 2 °C by sixfold over this near-term time frame and thereafter diverge strongly dependent on emission choices. Committed damages arise predominantly through changes in average temperature, but accounting for further climatic components raises estimates by approximately 50% and leads to stronger regional heterogeneity. Committed losses are projected for all regions except those at very high latitudes, at which reductions in temperature variability bring benefits. The largest losses are committed at lower latitudes in regions with lower cumulative historical emissions and lower present-day income.

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Projections of the macroeconomic damage caused by future climate change are crucial to informing public and policy debates about adaptation, mitigation and climate justice. On the one hand, adaptation against climate impacts must be justified and planned on the basis of an understanding of their future magnitude and spatial distribution 9 . This is also of importance in the context of climate justice 10 , as well as to key societal actors, including governments, central banks and private businesses, which increasingly require the inclusion of climate risks in their macroeconomic forecasts to aid adaptive decision-making 11 , 12 . On the other hand, climate mitigation policy such as the Paris Climate Agreement is often evaluated by balancing the costs of its implementation against the benefits of avoiding projected physical damages. This evaluation occurs both formally through cost–benefit analyses 1 , 4 , 5 , 6 , as well as informally through public perception of mitigation and damage costs 13 .

Projections of future damages meet challenges when informing these debates, in particular the human biases relating to uncertainty and remoteness that are raised by long-term perspectives 14 . Here we aim to overcome such challenges by assessing the extent of economic damages from climate change to which the world is already committed by historical emissions and socio-economic inertia (the range of future emission scenarios that are considered socio-economically plausible 15 ). Such a focus on the near term limits the large uncertainties about diverging future emission trajectories, the resulting long-term climate response and the validity of applying historically observed climate–economic relations over long timescales during which socio-technical conditions may change considerably. As such, this focus aims to simplify the communication and maximize the credibility of projected economic damages from future climate change.

In projecting the future economic damages from climate change, we make use of recent advances in climate econometrics that provide evidence for impacts on sub-national economic growth from numerous components of the distribution of daily temperature and precipitation 3 , 7 , 8 . Using fixed-effects panel regression models to control for potential confounders, these studies exploit within-region variation in local temperature and precipitation in a panel of more than 1,600 regions worldwide, comprising climate and income data over the past 40 years, to identify the plausibly causal effects of changes in several climate variables on economic productivity 16 , 17 . Specifically, macroeconomic impacts have been identified from changing daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall that occur in addition to those already identified from changing average temperature 2 , 3 , 18 . Moreover, regional heterogeneity in these effects based on the prevailing local climatic conditions has been found using interactions terms. The selection of these climate variables follows micro-level evidence for mechanisms related to the impacts of average temperatures on labour and agricultural productivity 2 , of temperature variability on agricultural productivity and health 7 , as well as of precipitation on agricultural productivity, labour outcomes and flood damages 8 (see Extended Data Table 1 for an overview, including more detailed references). References  7 , 8 contain a more detailed motivation for the use of these particular climate variables and provide extensive empirical tests about the robustness and nature of their effects on economic output, which are summarized in Methods . By accounting for these extra climatic variables at the sub-national level, we aim for a more comprehensive description of climate impacts with greater detail across both time and space.

Constraining the persistence of impacts

A key determinant and source of discrepancy in estimates of the magnitude of future climate damages is the extent to which the impact of a climate variable on economic growth rates persists. The two extreme cases in which these impacts persist indefinitely or only instantaneously are commonly referred to as growth or level effects 19 , 20 (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for mathematical definitions). Recent work shows that future damages from climate change depend strongly on whether growth or level effects are assumed 20 . Following refs.  2 , 18 , we provide constraints on this persistence by using distributed lag models to test the significance of delayed effects separately for each climate variable. Notably, and in contrast to refs.  2 , 18 , we use climate variables in their first-differenced form following ref.  3 , implying a dependence of the growth rate on a change in climate variables. This choice means that a baseline specification without any lags constitutes a model prior of purely level effects, in which a permanent change in the climate has only an instantaneous effect on the growth rate 3 , 19 , 21 . By including lags, one can then test whether any effects may persist further. This is in contrast to the specification used by refs.  2 , 18 , in which climate variables are used without taking the first difference, implying a dependence of the growth rate on the level of climate variables. In this alternative case, the baseline specification without any lags constitutes a model prior of pure growth effects, in which a change in climate has an infinitely persistent effect on the growth rate. Consequently, including further lags in this alternative case tests whether the initial growth impact is recovered 18 , 19 , 21 . Both of these specifications suffer from the limiting possibility that, if too few lags are included, one might falsely accept the model prior. The limitations of including a very large number of lags, including loss of data and increasing statistical uncertainty with an increasing number of parameters, mean that such a possibility is likely. By choosing a specification in which the model prior is one of level effects, our approach is therefore conservative by design, avoiding assumptions of infinite persistence of climate impacts on growth and instead providing a lower bound on this persistence based on what is observable empirically (see Methods section ‘Empirical model specification: fixed-effects distributed lag models’ for further exposition of this framework). The conservative nature of such a choice is probably the reason that ref.  19 finds much greater consistency between the impacts projected by models that use the first difference of climate variables, as opposed to their levels.

We begin our empirical analysis of the persistence of climate impacts on growth using ten lags of the first-differenced climate variables in fixed-effects distributed lag models. We detect substantial effects on economic growth at time lags of up to approximately 8–10 years for the temperature terms and up to approximately 4 years for the precipitation terms (Extended Data Fig. 1 and Extended Data Table 2 ). Furthermore, evaluation by means of information criteria indicates that the inclusion of all five climate variables and the use of these numbers of lags provide a preferable trade-off between best-fitting the data and including further terms that could cause overfitting, in comparison with model specifications excluding climate variables or including more or fewer lags (Extended Data Fig. 3 , Supplementary Methods Section  1 and Supplementary Table 1 ). We therefore remove statistically insignificant terms at later lags (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). Further tests using Monte Carlo simulations demonstrate that the empirical models are robust to autocorrelation in the lagged climate variables (Supplementary Methods Section  2 and Supplementary Figs. 4 and 5 ), that information criteria provide an effective indicator for lag selection (Supplementary Methods Section  2 and Supplementary Fig. 6 ), that the results are robust to concerns of imperfect multicollinearity between climate variables and that including several climate variables is actually necessary to isolate their separate effects (Supplementary Methods Section  3 and Supplementary Fig. 7 ). We provide a further robustness check using a restricted distributed lag model to limit oscillations in the lagged parameter estimates that may result from autocorrelation, finding that it provides similar estimates of cumulative marginal effects to the unrestricted model (Supplementary Methods Section 4 and Supplementary Figs. 8 and 9 ). Finally, to explicitly account for any outstanding uncertainty arising from the precise choice of the number of lags, we include empirical models with marginally different numbers of lags in the error-sampling procedure of our projection of future damages. On the basis of the lag-selection procedure (the significance of lagged terms in Extended Data Fig. 1 and Extended Data Table 2 , as well as information criteria in Extended Data Fig. 3 ), we sample from models with eight to ten lags for temperature and four for precipitation (models shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ). In summary, this empirical approach to constrain the persistence of climate impacts on economic growth rates is conservative by design in avoiding assumptions of infinite persistence, but nevertheless provides a lower bound on the extent of impact persistence that is robust to the numerous tests outlined above.

Committed damages until mid-century

We combine these empirical economic response functions (Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) with an ensemble of 21 climate models (see Supplementary Table 5 ) from the Coupled Model Intercomparison Project Phase 6 (CMIP-6) 22 to project the macroeconomic damages from these components of physical climate change (see Methods for further details). Bias-adjusted climate models that provide a highly accurate reproduction of observed climatological patterns with limited uncertainty (Supplementary Table 6 ) are used to avoid introducing biases in the projections. Following a well-developed literature 2 , 3 , 19 , these projections do not aim to provide a prediction of future economic growth. Instead, they are a projection of the exogenous impact of future climate conditions on the economy relative to the baselines specified by socio-economic projections, based on the plausibly causal relationships inferred by the empirical models and assuming ceteris paribus. Other exogenous factors relevant for the prediction of economic output are purposefully assumed constant.

A Monte Carlo procedure that samples from climate model projections, empirical models with different numbers of lags and model parameter estimates (obtained by 1,000 block-bootstrap resamples of each of the regressions in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) is used to estimate the combined uncertainty from these sources. Given these uncertainty distributions, we find that projected global damages are statistically indistinguishable across the two most extreme emission scenarios until 2049 (at the 5% significance level; Fig. 1 ). As such, the climate damages occurring before this time constitute those to which the world is already committed owing to the combination of past emissions and the range of future emission scenarios that are considered socio-economically plausible 15 . These committed damages comprise a permanent income reduction of 19% on average globally (population-weighted average) in comparison with a baseline without climate-change impacts (with a likely range of 11–29%, following the likelihood classification adopted by the Intergovernmental Panel on Climate Change (IPCC); see caption of Fig. 1 ). Even though levels of income per capita generally still increase relative to those of today, this constitutes a permanent income reduction for most regions, including North America and Europe (each with median income reductions of approximately 11%) and with South Asia and Africa being the most strongly affected (each with median income reductions of approximately 22%; Fig. 1 ). Under a middle-of-the road scenario of future income development (SSP2, in which SSP stands for Shared Socio-economic Pathway), this corresponds to global annual damages in 2049 of 38 trillion in 2005 international dollars (likely range of 19–59 trillion 2005 international dollars). Compared with empirical specifications that assume pure growth or pure level effects, our preferred specification that provides a robust lower bound on the extent of climate impact persistence produces damages between these two extreme assumptions (Extended Data Fig. 3 ).

Estimates of the projected reduction in income per capita from changes in all climate variables based on empirical models of climate impacts on economic output with a robust lower bound on their persistence (Extended Data Fig. 1 ) under a low-emission scenario compatible with the 2 °C warming target and a high-emission scenario (SSP2-RCP2.6 and SSP5-RCP8.5, respectively) are shown in purple and orange, respectively. Shading represents the 34% and 10% confidence intervals reflecting the likely and very likely ranges, respectively (following the likelihood classification adopted by the IPCC), having estimated uncertainty from a Monte Carlo procedure, which samples the uncertainty from the choice of physical climate models, empirical models with different numbers of lags and bootstrapped estimates of the regression parameters shown in Supplementary Figs. 1 – 3 . Vertical dashed lines show the time at which the climate damages of the two emission scenarios diverge at the 5% and 1% significance levels based on the distribution of differences between emission scenarios arising from the uncertainty sampling discussed above. Note that uncertainty in the difference of the two scenarios is smaller than the combined uncertainty of the two respective scenarios because samples of the uncertainty (climate model and empirical model choice, as well as model parameter bootstrap) are consistent across the two emission scenarios, hence the divergence of damages occurs while the uncertainty bounds of the two separate damage scenarios still overlap. Estimates of global mitigation costs from the three IAMs that provide results for the SSP2 baseline and SSP2-RCP2.6 scenario are shown in light green in the top panel, with the median of these estimates shown in bold.

Damages already outweigh mitigation costs

We compare the damages to which the world is committed over the next 25 years to estimates of the mitigation costs required to achieve the Paris Climate Agreement. Taking estimates of mitigation costs from the three integrated assessment models (IAMs) in the IPCC AR6 database 23 that provide results under comparable scenarios (SSP2 baseline and SSP2-RCP2.6, in which RCP stands for Representative Concentration Pathway), we find that the median committed climate damages are larger than the median mitigation costs in 2050 (six trillion in 2005 international dollars) by a factor of approximately six (note that estimates of mitigation costs are only provided every 10 years by the IAMs and so a comparison in 2049 is not possible). This comparison simply aims to compare the magnitude of future damages against mitigation costs, rather than to conduct a formal cost–benefit analysis of transitioning from one emission path to another. Formal cost–benefit analyses typically find that the net benefits of mitigation only emerge after 2050 (ref.  5 ), which may lead some to conclude that physical damages from climate change are simply not large enough to outweigh mitigation costs until the second half of the century. Our simple comparison of their magnitudes makes clear that damages are actually already considerably larger than mitigation costs and the delayed emergence of net mitigation benefits results primarily from the fact that damages across different emission paths are indistinguishable until mid-century (Fig. 1 ).

Although these near-term damages constitute those to which the world is already committed, we note that damage estimates diverge strongly across emission scenarios after 2049, conveying the clear benefits of mitigation from a purely economic point of view that have been emphasized in previous studies 4 , 24 . As well as the uncertainties assessed in Fig. 1 , these conclusions are robust to structural choices, such as the timescale with which changes in the moderating variables of the empirical models are estimated (Supplementary Figs. 10 and 11 ), as well as the order in which one accounts for the intertemporal and international components of currency comparison (Supplementary Fig. 12 ; see Methods for further details).

Damages from variability and extremes

Committed damages primarily arise through changes in average temperature (Fig. 2 ). This reflects the fact that projected changes in average temperature are larger than those in other climate variables when expressed as a function of their historical interannual variability (Extended Data Fig. 4 ). Because the historical variability is that on which the empirical models are estimated, larger projected changes in comparison with this variability probably lead to larger future impacts in a purely statistical sense. From a mechanistic perspective, one may plausibly interpret this result as implying that future changes in average temperature are the most unprecedented from the perspective of the historical fluctuations to which the economy is accustomed and therefore will cause the most damage. This insight may prove useful in terms of guiding adaptation measures to the sources of greatest damage.

Estimates of the median projected reduction in sub-national income per capita across emission scenarios (SSP2-RCP2.6 and SSP2-RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ). a , Impacts arising from all climate variables. b – f , Impacts arising separately from changes in annual mean temperature ( b ), daily temperature variability ( c ), total annual precipitation ( d ), the annual number of wet days (>1 mm) ( e ) and extreme daily rainfall ( f ) (see Methods for further definitions). Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Nevertheless, future damages based on empirical models that consider changes in annual average temperature only and exclude the other climate variables constitute income reductions of only 13% in 2049 (Extended Data Fig. 5a , likely range 5–21%). This suggests that accounting for the other components of the distribution of temperature and precipitation raises net damages by nearly 50%. This increase arises through the further damages that these climatic components cause, but also because their inclusion reveals a stronger negative economic response to average temperatures (Extended Data Fig. 5b ). The latter finding is consistent with our Monte Carlo simulations, which suggest that the magnitude of the effect of average temperature on economic growth is underestimated unless accounting for the impacts of other correlated climate variables (Supplementary Fig. 7 ).

In terms of the relative contributions of the different climatic components to overall damages, we find that accounting for daily temperature variability causes the largest increase in overall damages relative to empirical frameworks that only consider changes in annual average temperature (4.9 percentage points, likely range 2.4–8.7 percentage points, equivalent to approximately 10 trillion international dollars). Accounting for precipitation causes smaller increases in overall damages, which are—nevertheless—equivalent to approximately 1.2 trillion international dollars: 0.01 percentage points (−0.37–0.33 percentage points), 0.34 percentage points (0.07–0.90 percentage points) and 0.36 percentage points (0.13–0.65 percentage points) from total annual precipitation, the number of wet days and extreme daily precipitation, respectively. Moreover, climate models seem to underestimate future changes in temperature variability 25 and extreme precipitation 26 , 27 in response to anthropogenic forcing as compared with that observed historically, suggesting that the true impacts from these variables may be larger.

The distribution of committed damages

The spatial distribution of committed damages (Fig. 2a ) reflects a complex interplay between the patterns of future change in several climatic components and those of historical economic vulnerability to changes in those variables. Damages resulting from increasing annual mean temperature (Fig. 2b ) are negative almost everywhere globally, and larger at lower latitudes in regions in which temperatures are already higher and economic vulnerability to temperature increases is greatest (see the response heterogeneity to mean temperature embodied in Extended Data Fig. 1a ). This occurs despite the amplified warming projected at higher latitudes 28 , suggesting that regional heterogeneity in economic vulnerability to temperature changes outweighs heterogeneity in the magnitude of future warming (Supplementary Fig. 13a ). Economic damages owing to daily temperature variability (Fig. 2c ) exhibit a strong latitudinal polarisation, primarily reflecting the physical response of daily variability to greenhouse forcing in which increases in variability across lower latitudes (and Europe) contrast decreases at high latitudes 25 (Supplementary Fig. 13b ). These two temperature terms are the dominant determinants of the pattern of overall damages (Fig. 2a ), which exhibits a strong polarity with damages across most of the globe except at the highest northern latitudes. Future changes in total annual precipitation mainly bring economic benefits except in regions of drying, such as the Mediterranean and central South America (Fig. 2d and Supplementary Fig. 13c ), but these benefits are opposed by changes in the number of wet days, which produce damages with a similar pattern of opposite sign (Fig. 2e and Supplementary Fig. 13d ). By contrast, changes in extreme daily rainfall produce damages in all regions, reflecting the intensification of daily rainfall extremes over global land areas 29 , 30 (Fig. 2f and Supplementary Fig. 13e ).

The spatial distribution of committed damages implies considerable injustice along two dimensions: culpability for the historical emissions that have caused climate change and pre-existing levels of socio-economic welfare. Spearman’s rank correlations indicate that committed damages are significantly larger in countries with smaller historical cumulative emissions, as well as in regions with lower current income per capita (Fig. 3 ). This implies that those countries that will suffer the most from the damages already committed are those that are least responsible for climate change and which also have the least resources to adapt to it.

Estimates of the median projected change in national income per capita across emission scenarios (RCP2.6 and RCP8.5) as well as climate model, empirical model and model parameter uncertainty in the year in which climate damages diverge at the 5% level (2049, as identified in Fig. 1 ) are plotted against cumulative national emissions per capita in 2020 (from the Global Carbon Project) and coloured by national income per capita in 2020 (from the World Bank) in a and vice versa in b . In each panel, the size of each scatter point is weighted by the national population in 2020 (from the World Bank). Inset numbers indicate the Spearman’s rank correlation ρ and P -values for a hypothesis test whose null hypothesis is of no correlation, as well as the Spearman’s rank correlation weighted by national population.

To further quantify this heterogeneity, we assess the difference in committed damages between the upper and lower quartiles of regions when ranked by present income levels and historical cumulative emissions (using a population weighting to both define the quartiles and estimate the group averages). On average, the quartile of countries with lower income are committed to an income loss that is 8.9 percentage points (or 61%) greater than the upper quartile (Extended Data Fig. 6 ), with a likely range of 3.8–14.7 percentage points across the uncertainty sampling of our damage projections (following the likelihood classification adopted by the IPCC). Similarly, the quartile of countries with lower historical cumulative emissions are committed to an income loss that is 6.9 percentage points (or 40%) greater than the upper quartile, with a likely range of 0.27–12 percentage points. These patterns reemphasize the prevalence of injustice in climate impacts 31 , 32 , 33 in the context of the damages to which the world is already committed by historical emissions and socio-economic inertia.

Contextualizing the magnitude of damages

The magnitude of projected economic damages exceeds previous literature estimates 2 , 3 , arising from several developments made on previous approaches. Our estimates are larger than those of ref.  2 (see first row of Extended Data Table 3 ), primarily because of the facts that sub-national estimates typically show a steeper temperature response (see also refs.  3 , 34 ) and that accounting for other climatic components raises damage estimates (Extended Data Fig. 5 ). However, we note that our empirical approach using first-differenced climate variables is conservative compared with that of ref.  2 in regard to the persistence of climate impacts on growth (see introduction and Methods section ‘Empirical model specification: fixed-effects distributed lag models’), an important determinant of the magnitude of long-term damages 19 , 21 . Using a similar empirical specification to ref.  2 , which assumes infinite persistence while maintaining the rest of our approach (sub-national data and further climate variables), produces considerably larger damages (purple curve of Extended Data Fig. 3 ). Compared with studies that do take the first difference of climate variables 3 , 35 , our estimates are also larger (see second and third rows of Extended Data Table 3 ). The inclusion of further climate variables (Extended Data Fig. 5 ) and a sufficient number of lags to more adequately capture the extent of impact persistence (Extended Data Figs. 1 and 2 ) are the main sources of this difference, as is the use of specifications that capture nonlinearities in the temperature response when compared with ref.  35 . In summary, our estimates develop on previous studies by incorporating the latest data and empirical insights 7 , 8 , as well as in providing a robust empirical lower bound on the persistence of impacts on economic growth, which constitutes a middle ground between the extremes of the growth-versus-levels debate 19 , 21 (Extended Data Fig. 3 ).

Compared with the fraction of variance explained by the empirical models historically (<5%), the projection of reductions in income of 19% may seem large. This arises owing to the fact that projected changes in climatic conditions are much larger than those that were experienced historically, particularly for changes in average temperature (Extended Data Fig. 4 ). As such, any assessment of future climate-change impacts necessarily requires an extrapolation outside the range of the historical data on which the empirical impact models were evaluated. Nevertheless, these models constitute the most state-of-the-art methods for inference of plausibly causal climate impacts based on observed data. Moreover, we take explicit steps to limit out-of-sample extrapolation by capping the moderating variables of the interaction terms at the 95th percentile of the historical distribution (see Methods ). This avoids extrapolating the marginal effects outside what was observed historically. Given the nonlinear response of economic output to annual mean temperature (Extended Data Fig. 1 and Extended Data Table 2 ), this is a conservative choice that limits the magnitude of damages that we project. Furthermore, back-of-the-envelope calculations indicate that the projected damages are consistent with the magnitude and patterns of historical economic development (see Supplementary Discussion Section  5 ).

Missing impacts and spatial spillovers

Despite assessing several climatic components from which economic impacts have recently been identified 3 , 7 , 8 , this assessment of aggregate climate damages should not be considered comprehensive. Important channels such as impacts from heatwaves 31 , sea-level rise 36 , tropical cyclones 37 and tipping points 38 , 39 , as well as non-market damages such as those to ecosystems 40 and human health 41 , are not considered in these estimates. Sea-level rise is unlikely to be feasibly incorporated into empirical assessments such as this because historical sea-level variability is mostly small. Non-market damages are inherently intractable within our estimates of impacts on aggregate monetary output and estimates of these impacts could arguably be considered as extra to those identified here. Recent empirical work suggests that accounting for these channels would probably raise estimates of these committed damages, with larger damages continuing to arise in the global south 31 , 36 , 37 , 38 , 39 , 40 , 41 , 42 .

Moreover, our main empirical analysis does not explicitly evaluate the potential for impacts in local regions to produce effects that ‘spill over’ into other regions. Such effects may further mitigate or amplify the impacts we estimate, for example, if companies relocate production from one affected region to another or if impacts propagate along supply chains. The current literature indicates that trade plays a substantial role in propagating spillover effects 43 , 44 , making their assessment at the sub-national level challenging without available data on sub-national trade dependencies. Studies accounting for only spatially adjacent neighbours indicate that negative impacts in one region induce further negative impacts in neighbouring regions 45 , 46 , 47 , 48 , suggesting that our projected damages are probably conservative by excluding these effects. In Supplementary Fig. 14 , we assess spillovers from neighbouring regions using a spatial-lag model. For simplicity, this analysis excludes temporal lags, focusing only on contemporaneous effects. The results show that accounting for spatial spillovers can amplify the overall magnitude, and also the heterogeneity, of impacts. Consistent with previous literature, this indicates that the overall magnitude (Fig. 1 ) and heterogeneity (Fig. 3 ) of damages that we project in our main specification may be conservative without explicitly accounting for spillovers. We note that further analysis that addresses both spatially and trade-connected spillovers, while also accounting for delayed impacts using temporal lags, would be necessary to adequately address this question fully. These approaches offer fruitful avenues for further research but are beyond the scope of this manuscript, which primarily aims to explore the impacts of different climate conditions and their persistence.

Policy implications

We find that the economic damages resulting from climate change until 2049 are those to which the world economy is already committed and that these greatly outweigh the costs required to mitigate emissions in line with the 2 °C target of the Paris Climate Agreement (Fig. 1 ). This assessment is complementary to formal analyses of the net costs and benefits associated with moving from one emission path to another, which typically find that net benefits of mitigation only emerge in the second half of the century 5 . Our simple comparison of the magnitude of damages and mitigation costs makes clear that this is primarily because damages are indistinguishable across emissions scenarios—that is, committed—until mid-century (Fig. 1 ) and that they are actually already much larger than mitigation costs. For simplicity, and owing to the availability of data, we compare damages to mitigation costs at the global level. Regional estimates of mitigation costs may shed further light on the national incentives for mitigation to which our results already hint, of relevance for international climate policy. Although these damages are committed from a mitigation perspective, adaptation may provide an opportunity to reduce them. Moreover, the strong divergence of damages after mid-century reemphasizes the clear benefits of mitigation from a purely economic perspective, as highlighted in previous studies 1 , 4 , 6 , 24 .

Historical climate data

Historical daily 2-m temperature and precipitation totals (in mm) are obtained for the period 1979–2019 from the W5E5 database. The W5E5 dataset comes from ERA-5, a state-of-the-art reanalysis of historical observations, but has been bias-adjusted by applying version 2.0 of the WATCH Forcing Data to ERA-5 reanalysis data and precipitation data from version 2.3 of the Global Precipitation Climatology Project to better reflect ground-based measurements 49 , 50 , 51 . We obtain these data on a 0.5° × 0.5° grid from the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) database. Notably, these historical data have been used to bias-adjust future climate projections from CMIP-6 (see the following section), ensuring consistency between the distribution of historical daily weather on which our empirical models were estimated and the climate projections used to estimate future damages. These data are publicly available from the ISIMIP database. See refs.  7 , 8 for robustness tests of the empirical models to the choice of climate data reanalysis products.

Future climate data

Daily 2-m temperature and precipitation totals (in mm) are taken from 21 climate models participating in CMIP-6 under a high (RCP8.5) and a low (RCP2.6) greenhouse gas emission scenario from 2015 to 2100. The data have been bias-adjusted and statistically downscaled to a common half-degree grid to reflect the historical distribution of daily temperature and precipitation of the W5E5 dataset using the trend-preserving method developed by the ISIMIP 50 , 52 . As such, the climate model data reproduce observed climatological patterns exceptionally well (Supplementary Table 5 ). Gridded data are publicly available from the ISIMIP database.

Historical economic data

Historical economic data come from the DOSE database of sub-national economic output 53 . We use a recent revision to the DOSE dataset that provides data across 83 countries, 1,660 sub-national regions with varying temporal coverage from 1960 to 2019. Sub-national units constitute the first administrative division below national, for example, states for the USA and provinces for China. Data come from measures of gross regional product per capita (GRPpc) or income per capita in local currencies, reflecting the values reported in national statistical agencies, yearbooks and, in some cases, academic literature. We follow previous literature 3 , 7 , 8 , 54 and assess real sub-national output per capita by first converting values from local currencies to US dollars to account for diverging national inflationary tendencies and then account for US inflation using a US deflator. Alternatively, one might first account for national inflation and then convert between currencies. Supplementary Fig. 12 demonstrates that our conclusions are consistent when accounting for price changes in the reversed order, although the magnitude of estimated damages varies. See the documentation of the DOSE dataset for further discussion of these choices. Conversions between currencies are conducted using exchange rates from the FRED database of the Federal Reserve Bank of St. Louis 55 and the national deflators from the World Bank 56 .

Future socio-economic data

Baseline gridded gross domestic product (GDP) and population data for the period 2015–2100 are taken from the middle-of-the-road scenario SSP2 (ref.  15 ). Population data have been downscaled to a half-degree grid by the ISIMIP following the methodologies of refs.  57 , 58 , which we then aggregate to the sub-national level of our economic data using the spatial aggregation procedure described below. Because current methodologies for downscaling the GDP of the SSPs use downscaled population to do so, per-capita estimates of GDP with a realistic distribution at the sub-national level are not readily available for the SSPs. We therefore use national-level GDP per capita (GDPpc) projections for all sub-national regions of a given country, assuming homogeneity within countries in terms of baseline GDPpc. Here we use projections that have been updated to account for the impact of the COVID-19 pandemic on the trajectory of future income, while remaining consistent with the long-term development of the SSPs 59 . The choice of baseline SSP alters the magnitude of projected climate damages in monetary terms, but when assessed in terms of percentage change from the baseline, the choice of socio-economic scenario is inconsequential. Gridded SSP population data and national-level GDPpc data are publicly available from the ISIMIP database. Sub-national estimates as used in this study are available in the code and data replication files.

Climate variables

Following recent literature 3 , 7 , 8 , we calculate an array of climate variables for which substantial impacts on macroeconomic output have been identified empirically, supported by further evidence at the micro level for plausible underlying mechanisms. See refs.  7 , 8 for an extensive motivation for the use of these particular climate variables and for detailed empirical tests on the nature and robustness of their effects on economic output. To summarize, these studies have found evidence for independent impacts on economic growth rates from annual average temperature, daily temperature variability, total annual precipitation, the annual number of wet days and extreme daily rainfall. Assessments of daily temperature variability were motivated by evidence of impacts on agricultural output and human health, as well as macroeconomic literature on the impacts of volatility on growth when manifest in different dimensions, such as government spending, exchange rates and even output itself 7 . Assessments of precipitation impacts were motivated by evidence of impacts on agricultural productivity, metropolitan labour outcomes and conflict, as well as damages caused by flash flooding 8 . See Extended Data Table 1 for detailed references to empirical studies of these physical mechanisms. Marked impacts of daily temperature variability, total annual precipitation, the number of wet days and extreme daily rainfall on macroeconomic output were identified robustly across different climate datasets, spatial aggregation schemes, specifications of regional time trends and error-clustering approaches. They were also found to be robust to the consideration of temperature extremes 7 , 8 . Furthermore, these climate variables were identified as having independent effects on economic output 7 , 8 , which we further explain here using Monte Carlo simulations to demonstrate the robustness of the results to concerns of imperfect multicollinearity between climate variables (Supplementary Methods Section  2 ), as well as by using information criteria (Supplementary Table 1 ) to demonstrate that including several lagged climate variables provides a preferable trade-off between optimally describing the data and limiting the possibility of overfitting.

We calculate these variables from the distribution of daily, d , temperature, T x , d , and precipitation, P x , d , at the grid-cell, x , level for both the historical and future climate data. As well as annual mean temperature, \({\bar{T}}_{x,y}\) , and annual total precipitation, P x , y , we calculate annual, y , measures of daily temperature variability, \({\widetilde{T}}_{x,y}\) :

the number of wet days, Pwd x , y :

and extreme daily rainfall:

in which T x , d , m , y is the grid-cell-specific daily temperature in month m and year y , \({\bar{T}}_{x,m,{y}}\) is the year and grid-cell-specific monthly, m , mean temperature, D m and D y the number of days in a given month m or year y , respectively, H the Heaviside step function, 1 mm the threshold used to define wet days and P 99.9 x is the 99.9th percentile of historical (1979–2019) daily precipitation at the grid-cell level. Units of the climate measures are degrees Celsius for annual mean temperature and daily temperature variability, millimetres for total annual precipitation and extreme daily precipitation, and simply the number of days for the annual number of wet days.

We also calculated weighted standard deviations of monthly rainfall totals as also used in ref.  8 but do not include them in our projections as we find that, when accounting for delayed effects, their effect becomes statistically indistinct and is better captured by changes in total annual rainfall.

Spatial aggregation

We aggregate grid-cell-level historical and future climate measures, as well as grid-cell-level future GDPpc and population, to the level of the first administrative unit below national level of the GADM database, using an area-weighting algorithm that estimates the portion of each grid cell falling within an administrative boundary. We use this as our baseline specification following previous findings that the effect of area or population weighting at the sub-national level is negligible 7 , 8 .

Empirical model specification: fixed-effects distributed lag models

Following a wide range of climate econometric literature 16 , 60 , we use panel regression models with a selection of fixed effects and time trends to isolate plausibly exogenous variation with which to maximize confidence in a causal interpretation of the effects of climate on economic growth rates. The use of region fixed effects, μ r , accounts for unobserved time-invariant differences between regions, such as prevailing climatic norms and growth rates owing to historical and geopolitical factors. The use of yearly fixed effects, η y , accounts for regionally invariant annual shocks to the global climate or economy such as the El Niño–Southern Oscillation or global recessions. In our baseline specification, we also include region-specific linear time trends, k r y , to exclude the possibility of spurious correlations resulting from common slow-moving trends in climate and growth.

The persistence of climate impacts on economic growth rates is a key determinant of the long-term magnitude of damages. Methods for inferring the extent of persistence in impacts on growth rates have typically used lagged climate variables to evaluate the presence of delayed effects or catch-up dynamics 2 , 18 . For example, consider starting from a model in which a climate condition, C r , y , (for example, annual mean temperature) affects the growth rate, Δlgrp r , y (the first difference of the logarithm of gross regional product) of region r in year y :

which we refer to as a ‘pure growth effects’ model in the main text. Typically, further lags are included,

and the cumulative effect of all lagged terms is evaluated to assess the extent to which climate impacts on growth rates persist. Following ref.  18 , in the case that,

the implication is that impacts on the growth rate persist up to NL years after the initial shock (possibly to a weaker or a stronger extent), whereas if

then the initial impact on the growth rate is recovered after NL years and the effect is only one on the level of output. However, we note that such approaches are limited by the fact that, when including an insufficient number of lags to detect a recovery of the growth rates, one may find equation ( 6 ) to be satisfied and incorrectly assume that a change in climatic conditions affects the growth rate indefinitely. In practice, given a limited record of historical data, including too few lags to confidently conclude in an infinitely persistent impact on the growth rate is likely, particularly over the long timescales over which future climate damages are often projected 2 , 24 . To avoid this issue, we instead begin our analysis with a model for which the level of output, lgrp r , y , depends on the level of a climate variable, C r , y :

Given the non-stationarity of the level of output, we follow the literature 19 and estimate such an equation in first-differenced form as,

which we refer to as a model of ‘pure level effects’ in the main text. This model constitutes a baseline specification in which a permanent change in the climate variable produces an instantaneous impact on the growth rate and a permanent effect only on the level of output. By including lagged variables in this specification,

we are able to test whether the impacts on the growth rate persist any further than instantaneously by evaluating whether α L  > 0 are statistically significantly different from zero. Even though this framework is also limited by the possibility of including too few lags, the choice of a baseline model specification in which impacts on the growth rate do not persist means that, in the case of including too few lags, the framework reverts to the baseline specification of level effects. As such, this framework is conservative with respect to the persistence of impacts and the magnitude of future damages. It naturally avoids assumptions of infinite persistence and we are able to interpret any persistence that we identify with equation ( 9 ) as a lower bound on the extent of climate impact persistence on growth rates. See the main text for further discussion of this specification choice, in particular about its conservative nature compared with previous literature estimates, such as refs.  2 , 18 .

We allow the response to climatic changes to vary across regions, using interactions of the climate variables with historical average (1979–2019) climatic conditions reflecting heterogenous effects identified in previous work 7 , 8 . Following this previous work, the moderating variables of these interaction terms constitute the historical average of either the variable itself or of the seasonal temperature difference, \({\hat{T}}_{r}\) , or annual mean temperature, \({\bar{T}}_{r}\) , in the case of daily temperature variability 7 and extreme daily rainfall, respectively 8 .

The resulting regression equation with N and M lagged variables, respectively, reads:

in which Δlgrp r , y is the annual, regional GRPpc growth rate, measured as the first difference of the logarithm of real GRPpc, following previous work 2 , 3 , 7 , 8 , 18 , 19 . Fixed-effects regressions were run using the fixest package in R (ref.  61 ).

Estimates of the coefficients of interest α i , L are shown in Extended Data Fig. 1 for N  =  M  = 10 lags and for our preferred choice of the number of lags in Supplementary Figs. 1 – 3 . In Extended Data Fig. 1 , errors are shown clustered at the regional level, but for the construction of damage projections, we block-bootstrap the regressions by region 1,000 times to provide a range of parameter estimates with which to sample the projection uncertainty (following refs.  2 , 31 ).

Spatial-lag model

In Supplementary Fig. 14 , we present the results from a spatial-lag model that explores the potential for climate impacts to ‘spill over’ into spatially neighbouring regions. We measure the distance between centroids of each pair of sub-national regions and construct spatial lags that take the average of the first-differenced climate variables and their interaction terms over neighbouring regions that are at distances of 0–500, 500–1,000, 1,000–1,500 and 1,500–2000 km (spatial lags, ‘SL’, 1 to 4). For simplicity, we then assess a spatial-lag model without temporal lags to assess spatial spillovers of contemporaneous climate impacts. This model takes the form:

in which SL indicates the spatial lag of each climate variable and interaction term. In Supplementary Fig. 14 , we plot the cumulative marginal effect of each climate variable at different baseline climate conditions by summing the coefficients for each climate variable and interaction term, for example, for average temperature impacts as:

These cumulative marginal effects can be regarded as the overall spatially dependent impact to an individual region given a one-unit shock to a climate variable in that region and all neighbouring regions at a given value of the moderating variable of the interaction term.

Constructing projections of economic damage from future climate change

We construct projections of future climate damages by applying the coefficients estimated in equation ( 10 ) and shown in Supplementary Tables 2 – 4 (when including only lags with statistically significant effects in specifications that limit overfitting; see Supplementary Methods Section  1 ) to projections of future climate change from the CMIP-6 models. Year-on-year changes in each primary climate variable of interest are calculated to reflect the year-to-year variations used in the empirical models. 30-year moving averages of the moderating variables of the interaction terms are calculated to reflect the long-term average of climatic conditions that were used for the moderating variables in the empirical models. By using moving averages in the projections, we account for the changing vulnerability to climate shocks based on the evolving long-term conditions (Supplementary Figs. 10 and 11 show that the results are robust to the precise choice of the window of this moving average). Although these climate variables are not differenced, the fact that the bias-adjusted climate models reproduce observed climatological patterns across regions for these moderating variables very accurately (Supplementary Table 6 ) with limited spread across models (<3%) precludes the possibility that any considerable bias or uncertainty is introduced by this methodological choice. However, we impose caps on these moderating variables at the 95th percentile at which they were observed in the historical data to prevent extrapolation of the marginal effects outside the range in which the regressions were estimated. This is a conservative choice that limits the magnitude of our damage projections.

Time series of primary climate variables and moderating climate variables are then combined with estimates of the empirical model parameters to evaluate the regression coefficients in equation ( 10 ), producing a time series of annual GRPpc growth-rate reductions for a given emission scenario, climate model and set of empirical model parameters. The resulting time series of growth-rate impacts reflects those occurring owing to future climate change. By contrast, a future scenario with no climate change would be one in which climate variables do not change (other than with random year-to-year fluctuations) and hence the time-averaged evaluation of equation ( 10 ) would be zero. Our approach therefore implicitly compares the future climate-change scenario to this no-climate-change baseline scenario.

The time series of growth-rate impacts owing to future climate change in region r and year y , δ r , y , are then added to the future baseline growth rates, π r , y (in log-diff form), obtained from the SSP2 scenario to yield trajectories of damaged GRPpc growth rates, ρ r , y . These trajectories are aggregated over time to estimate the future trajectory of GRPpc with future climate impacts:

in which GRPpc r , y =2020 is the initial log level of GRPpc. We begin damage estimates in 2020 to reflect the damages occurring since the end of the period for which we estimate the empirical models (1979–2019) and to match the timing of mitigation-cost estimates from most IAMs (see below).

For each emission scenario, this procedure is repeated 1,000 times while randomly sampling from the selection of climate models, the selection of empirical models with different numbers of lags (shown in Supplementary Figs. 1 – 3 and Supplementary Tables 2 – 4 ) and bootstrapped estimates of the regression parameters. The result is an ensemble of future GRPpc trajectories that reflect uncertainty from both physical climate change and the structural and sampling uncertainty of the empirical models.

Estimates of mitigation costs

We obtain IPCC estimates of the aggregate costs of emission mitigation from the AR6 Scenario Explorer and Database hosted by IIASA 23 . Specifically, we search the AR6 Scenarios Database World v1.1 for IAMs that provided estimates of global GDP and population under both a SSP2 baseline and a SSP2-RCP2.6 scenario to maintain consistency with the socio-economic and emission scenarios of the climate damage projections. We find five IAMs that provide data for these scenarios, namely, MESSAGE-GLOBIOM 1.0, REMIND-MAgPIE 1.5, AIM/GCE 2.0, GCAM 4.2 and WITCH-GLOBIOM 3.1. Of these five IAMs, we use the results only from the first three that passed the IPCC vetting procedure for reproducing historical emission and climate trajectories. We then estimate global mitigation costs as the percentage difference in global per capita GDP between the SSP2 baseline and the SSP2-RCP2.6 emission scenario. In the case of one of these IAMs, estimates of mitigation costs begin in 2020, whereas in the case of two others, mitigation costs begin in 2010. The mitigation cost estimates before 2020 in these two IAMs are mostly negligible, and our choice to begin comparison with damage estimates in 2020 is conservative with respect to the relative weight of climate damages compared with mitigation costs for these two IAMs.

Data availability

Data on economic production and ERA-5 climate data are publicly available at https://doi.org/10.5281/zenodo.4681306 (ref. 62 ) and https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5 , respectively. Data on mitigation costs are publicly available at https://data.ene.iiasa.ac.at/ar6/#/downloads . Processed climate and economic data, as well as all other necessary data for reproduction of the results, are available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

Code availability

All code necessary for reproduction of the results is available at the public repository https://doi.org/10.5281/zenodo.10562951  (ref. 63 ).

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Acknowledgements

We gratefully acknowledge financing from the Volkswagen Foundation and the Deutsche Gesellschaft für Internationale Zusammenarbeit (GIZ) GmbH on behalf of the Government of the Federal Republic of Germany and Federal Ministry for Economic Cooperation and Development (BMZ).

Open access funding provided by Potsdam-Institut für Klimafolgenforschung (PIK) e.V.

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All authors contributed to the design of the analysis. M.K. conducted the analysis and produced the figures. All authors contributed to the interpretation and presentation of the results. M.K. and L.W. wrote the manuscript.

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Extended data figures and tables

Extended data fig. 1 constraining the persistence of historical climate impacts on economic growth rates..

The results of a panel-based fixed-effects distributed lag model for the effects of annual mean temperature ( a ), daily temperature variability ( b ), total annual precipitation ( c ), the number of wet days ( d ) and extreme daily precipitation ( e ) on sub-national economic growth rates. Point estimates show the effects of a 1 °C or one standard deviation increase (for temperature and precipitation variables, respectively) at the lower quartile, median and upper quartile of the relevant moderating variable (green, orange and purple, respectively) at different lagged periods after the initial shock (note that these are not cumulative effects). Climate variables are used in their first-differenced form (see main text for discussion) and the moderating climate variables are the annual mean temperature, seasonal temperature difference, total annual precipitation, number of wet days and annual mean temperature, respectively, in panels a – e (see Methods for further discussion). Error bars show the 95% confidence intervals having clustered standard errors by region. The within-region R 2 , Bayesian and Akaike information criteria for the model are shown at the top of the figure. This figure shows results with ten lags for each variable to demonstrate the observed levels of persistence, but our preferred specifications remove later lags based on the statistical significance of terms shown above and the information criteria shown in Extended Data Fig. 2 . The resulting models without later lags are shown in Supplementary Figs. 1 – 3 .

Extended Data Fig. 2 Incremental lag-selection procedure using information criteria and within-region R 2 .

Starting from a panel-based fixed-effects distributed lag model estimating the effects of climate on economic growth using the real historical data (as in equation ( 4 )) with ten lags for all climate variables (as shown in Extended Data Fig. 1 ), lags are incrementally removed for one climate variable at a time. The resulting Bayesian and Akaike information criteria are shown in a – e and f – j , respectively, and the within-region R 2 and number of observations in k – o and p – t , respectively. Different rows show the results when removing lags from different climate variables, ordered from top to bottom as annual mean temperature, daily temperature variability, total annual precipitation, the number of wet days and extreme annual precipitation. Information criteria show minima at approximately four lags for precipitation variables and ten to eight for temperature variables, indicating that including these numbers of lags does not lead to overfitting. See Supplementary Table 1 for an assessment using information criteria to determine whether including further climate variables causes overfitting.

Extended Data Fig. 3 Damages in our preferred specification that provides a robust lower bound on the persistence of climate impacts on economic growth versus damages in specifications of pure growth or pure level effects.

Estimates of future damages as shown in Fig. 1 but under the emission scenario RCP8.5 for three separate empirical specifications: in orange our preferred specification, which provides an empirical lower bound on the persistence of climate impacts on economic growth rates while avoiding assumptions of infinite persistence (see main text for further discussion); in purple a specification of ‘pure growth effects’ in which the first difference of climate variables is not taken and no lagged climate variables are included (the baseline specification of ref.  2 ); and in pink a specification of ‘pure level effects’ in which the first difference of climate variables is taken but no lagged terms are included.

Extended Data Fig. 4 Climate changes in different variables as a function of historical interannual variability.

Changes in each climate variable of interest from 1979–2019 to 2035–2065 under the high-emission scenario SSP5-RCP8.5, expressed as a percentage of the historical variability of each measure. Historical variability is estimated as the standard deviation of each detrended climate variable over the period 1979–2019 during which the empirical models were identified (detrending is appropriate because of the inclusion of region-specific linear time trends in the empirical models). See Supplementary Fig. 13 for changes expressed in standard units. Data on national administrative boundaries are obtained from the GADM database version 3.6 and are freely available for academic use ( https://gadm.org/ ).

Extended Data Fig. 5 Contribution of different climate variables to overall committed damages.

a , Climate damages in 2049 when using empirical models that account for all climate variables, changes in annual mean temperature only or changes in both annual mean temperature and one other climate variable (daily temperature variability, total annual precipitation, the number of wet days and extreme daily precipitation, respectively). b , The cumulative marginal effects of an increase in annual mean temperature of 1 °C, at different baseline temperatures, estimated from empirical models including all climate variables or annual mean temperature only. Estimates and uncertainty bars represent the median and 95% confidence intervals obtained from 1,000 block-bootstrap resamples from each of three different empirical models using eight, nine or ten lags of temperature terms.

Extended Data Fig. 6 The difference in committed damages between the upper and lower quartiles of countries when ranked by GDP and cumulative historical emissions.

Quartiles are defined using a population weighting, as are the average committed damages across each quartile group. The violin plots indicate the distribution of differences between quartiles across the two extreme emission scenarios (RCP2.6 and RCP8.5) and the uncertainty sampling procedure outlined in Methods , which accounts for uncertainty arising from the choice of lags in the empirical models, uncertainty in the empirical model parameter estimates, as well as the climate model projections. Bars indicate the median, as well as the 10th and 90th percentiles and upper and lower sixths of the distribution reflecting the very likely and likely ranges following the likelihood classification adopted by the IPCC.

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Kotz, M., Levermann, A. & Wenz, L. The economic commitment of climate change. Nature 628 , 551–557 (2024). https://doi.org/10.1038/s41586-024-07219-0

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Published on 24.4.2024 in Vol 26 (2024)

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Lisa Pilgram   1, 2 , MD ;   Thierry Meurers   3 , MSc ;   Bradley Malin   4 , PhD ;   Elke Schaeffner   5 , MSc, MD ;   Kai-Uwe Eckardt   2, 6 , MD ;   Fabian Prasser   3 , PhD ;   GCKD Investigators   7

1 Junior Digital Clinician Scientist Program, Biomedical Innovation Academy, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany

2 Department of Nephrology and Medical Intensive Care, Charité—Universitätsmedizin Berlin, Berlin, Germany

3 Medical Informatics Group, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany

4 Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States

5 Institute of Public Health, Charité—Universitätsmedizin Berlin, Berlin, Germany

6 Department of Nephrology and Hypertension, Universitätsklinikum Erlangen, Friedrich-Alexander University Erlangen-Nürnberg, Erlangen, Germany

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Germany rejects allegations that it’s facilitating acts of genocide in Gaza at UN court

Two days of preliminary hearings have wrapped up at the International Court of Justice in a case filed by Nicaragua against Germany that accuses Berlin of facilitating breaches of the Geneva Convention and international humanitarian law by providing arms and other support to Israel in its assault on Gaza.

A pro-Palestinian activist works on a protest poster near the International Court of Justice, or World Court, in The Hague, Netherlands, Monday, April 8, 2024, prior to the start of a two days hearing in a case brought by Nicaragua accusing Germany of breaching the genocide convention by providing arms and support to Israel. (AP Photo/Patrick Post)

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Tania von Uslar-Gleichen, Germany’s legal adviser and Director-General for Legal Affairs of the German Foreign Ministry, center, shakes hands with Alain Pellet, left, a lawyer representing Nicaragua, prior to the start of a two days hearing at the World Court in The Hague, Netherlands, Monday, April 8, 2024, in a case brought by Nicaragua accusing Germany of breaching the genocide convention by providing arms and support to Israel. (AP Photo/Patrick Post)

Nicaragua’s Ambassador Carlos Jose Arguello Gomez, right, and Alain Pellet, center, a lawyer representing Nicaragua, arrive for the start of a two days hearing at the World Court in The Hague, Netherlands, Monday, April 8, 2024, in a case brought by Nicaragua accusing Germany of breaching the genocide convention by providing arms and support to Israel. (AP Photo/Patrick Post)

Judge Nawaf Salam, third right, speaks at the start of a two days hearing at the World Court in The Hague, Netherlands, Monday, April 8, 2024, in a case brought by Nicaragua accusing Germany of breaching the genocide convention by providing arms and support to Israel. (AP Photo/Patrick Post)

Judge Nawaf Salam, center, speaks at the start of a two days hearing at the World Court in The Hague, Netherlands, Monday, April 8, 2024, in a case brought by Nicaragua accusing Germany of breaching the genocide convention by providing arms and support to Israel. (AP Photo/Patrick Post)

THE HAGUE, Netherlands (AP) — Germany on Tuesday strongly rejected a case brought by Nicaragua at the United Nations’ top court accusing Berlin of facilitating breaches of the Geneva Convention and international humanitarian law by providing arms and other support to Israel in its deadly assault on Gaza.

“The minute we look closely, Nicaragua’s accusations fall apart,” Christian Tams, a member of Germany’s legal team, told the 16-judge panel at the International Court of Justice.

On Monday, Nicaragua urged judges to order a halt to German military aid to Israel, arguing that Berlin’s support enables acts of genocide and breaches of international humanitarian law in Gaza .

The head of Germany’s legal team, Tania von Uslar-Gleichen, said Nicaragua’s claims “have no basis in fact or law. They are dependent on an assessment of conduct by Israel, not a party to these proceedings.”

Preliminary hearings held Monday and Tuesday are focused solely on Nicaragua’s request for so-called provisional measures, including a court order for Berlin to halt military and other aid to Israel and reinstate funding to the U.N. aid agency in Gaza.

Closing Germany’s arguments, Von Uslar-Gleichen urged judges not to impose preliminary measures and to toss out Nicaragua’s case.

Tams said that Germany had licensed only four exports of weapons of war to Israel since October, “three of which concern test or practice equipment.” He said 98% of military exports to Israel since the Oct. 7 attacks were not weapons of war, but other equipment.

Showing judges a photo of German aid being airdropped over Gaza, Tams added that Berlin continues to provide humanitarian support to Palestinians “every single day under extremely difficult conditions, constructively engaging with international partners.”

Nicaragua’s case is the latest legal attempt to rein in Israel’s offensive by a country with historic ties to the Palestinian people, after South Africa accused Israel of genocide at the same court late last year. It also comes against a backdrop of growing calls for Israel’s allies to stop supplying the country with weapons — and as some supporters, including Germany, have grown more critical of the war.

Speaking in Berlin, German Foreign Minister Annalena Baerbock told reporters that “from day one after Oct. 7, Germany has faced up to the incredible dilemma that Hamas deliberately entrenched itself behind civilians, deliberately used the human suffering of Palestinians and Palestinians in Gaza to expand its attack on Israel.”

Echoing comments by the German lawyers in court, Baerbock added that Germany is committed to international law, including the right to self-defense.

“This means that Israel has the right to defend itself, like every country in the world, against these terrorist attacks that continue to be carried out with the aim of destroying Israel as a state,” she said.

At Monday’s hearings, Nicaragua’s Ambassador to the Netherlands, Carlos José Argüello Gómez, accused Germany of “failing to honor its own obligation to prevent genocide or to ensure respect of international humanitarian law.”

However, another lawyer for Germany, Samuel Wordsworth, argued that the court could not rule Germany was violating the obligation to prevent genocide because its judges have not ruled that Israel is breaching the Genocide Convention.

In a preliminary phase of the case brought late last year by South Africa, the U.N. court has said that it is “plausible” that Israel’s actions in Gaza could amount to breaches of the convention.

“How can it be said that there was a failure to ensure respect of a third state, if the failure on the part of that third state to respect is not established in the first place?” Wordsworth said.

The court will likely take weeks to deliver its preliminary decision, and Nicaragua’s case will probably drag on for years.

Israel strongly denies that its assault amounts to genocidal acts, saying it is acting in self defense after Hamas-led militants stormed into southern Israel on Oct. 7 , killing some 1,200 people.

Since then, more than 33,000 Palestinians have been killed in Gaza, according to the territory’s Health Ministry. Its toll doesn’t differentiate between civilians and combatants, but it has said women and children make up the majority of the dead.

According to the Stockholm International Peace Research Institute, Germany is second only to the U.S. in supplying arms to Israel — but it would be harder, if not impossible, for the U.S. to be brought before the court because Washington does not recognize the ICJ’s power to compel countries to appear before it. The U.S. also has not signed a protocol to the Genocide Convention that allows countries to bring disputes to the court.

Associated Press writer Kirsten Grieshaber in Berlin contributed to this report.