research paper on economic growth

This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here .

• Browse All Articles
• Newsletter Sign-Up

EconomicGrowth →

No results found in working knowledge.

• Were any results found in one of the other content buckets on the left?
• Try removing some search filters.
• Use different search filters.

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List

Literature review on income inequality and economic growth

This paper provides a comprehensive literature review of the relationship between income inequality and economic growth. In the theoretical literature, we identified various models in which income inequality is linked to economic growth. They include (i) The level of economic development, (ii) The level of technological development, (iii) Social-political unrest, (iv) The savings rate, (v) The imperfection of credit markets, (vi) The political economy, (vii) Institutions and (viii) The fertility rate. Based on the transmission mechanisms of these models, we found that the relationship between income inequality and growth can be negative, positive or inconclusive. The first three models demonstrate that the relationship is inconclusive, the fourth shows that it is positive, while the remainder indicate that the relationship should be negative. In the face of theoretical ambiguity, we also noted that the empirical findings on the relationship between income inequality and growth are highly debatable. These findings can be broadly classified into four categories, namely negative, positive, inconclusive and no relationship. Based on these findings, we provide a critical survey on methodology issues employed in the prior studies and propose a better methodology to researchers for future studies.

• • Theoretical and empirical literature is reviewed and synthesis is done to understand the income inequality-growth nexus

Graphical Abstract

Specification table

Across countries, the unequal distribution of income and resources among the population is the defining challenge of our time. In both developed and developing economies, the income inequality gap (as measured by the decile ratios and the Gini coefficient based on the Lorenz curve) between rich and poor is at high levels, and continues to rise [24] . When income inequality becomes extremely high, it fuels social dissatisfaction and raises the threat of social and political unrest [13] . In similar vein, Alesina and Perotti [8] :1 argue that high income inequality, “ by increasing the probability of coups, revolutions, mass violence or, more generally, by increasing policy uncertainty and threatening property rights, has a negative effect on investment and, as a consequence, reduces growth ”.

Given the already high level of income inequality and rising trends in many countries, along with the potentially negative consequences for economies, we found that a significant body of literature examines the causes of income inequality and its consequences for economic development. Among them were theoretical analyses of the inequality–growth nexus, which identified various transmission mechanisms linking income inequality to economic growth. These include (i) The level of economic development, (ii) The level of technological development, (iii) Social-political unrest, (iv) The savings rate, (v) The imperfection of credit markets, (vi) The political economy, (vii) Institutions and (viii) The fertility rate. Based on these models, we found that the relationship between income inequality and growth can be negative, positive or inconclusive. Theories on the level of economic development (see [7 , 31 , 38 , 54] ) and technological development (see [6 , 29 , 33] ) demonstrate that the relationship between inequality and growth changes from positive to negative as the level of development increases. Inconclusive results are also echoed by the social-political unrest model, which argues that the socio-political unrest stemming from high income inequality can either inhibit or benefit growth (see [13 , 14 , 56 , 62] ). In addition, theories on the political economy (see [9 , 11 , 13 , 46 , 48 , 50] ), the imperfection of credit markets (see [5 , 12 , 30 , 51] ; Panniza, [46] ), institutions (see [22 , 34 , 61] ) and the fertility rate (see [26] ) demonstrate that income inequality is negatively related to growth. The only theory which supports the positive relationship between income inequality and growth is the theory on the savings rate (see [3 , 17 , 42 , 53] ).

Given such theoretical ambiguity, it is little wonder that the empirical findings on the relationship between income inequality and growth are strongly debated. Early empirical studies by Alesina and Rodrik [9] , Persson and Tabellini [50] and Perotti [49] reported that inequality exerted a negative impact on growth. That negative relationship has been confirmed by numerous subsequent studies (see, for example, Panniza, [18 , 19 , 46 , 55 , 64] ). Evidence of a negative relationship has, however, been challenged by studies which reported positive results on the inequality–growth nexus (see, for example, [28 , 39 , 57 , 58] ). In addition, several studies have yielded inconclusive findings, with most reporting that the relationship is positive in high-income and negative in low-income countries (see, for example, [13 , 20 , 25 , 27] ). A few studies found no relationship between inequality and growth (see [15 , 44] ).

Given the above background, the aim here is to provide a comprehensive literature review of the relationship between income inequality and economic growth, both in theory and empirically. While Section 2 critically analyses the theoretical framework of the income inequality–growth nexus, Section 3 reviews empirical studies on this relationship. Section 4 provides a critical survey on methodology issues employed in the prior studies and proposes a better methodology that can help reconcile the literature. Section 5 concludes the study.

Income inequality and economic growth: Theoretical framework

A theoretical analysis of the inequality–growth nexus has identified various transmission mechanisms in which income inequality is linked to economic growth. These mechanisms are discussed in detail in this section.

The level of economic development

Early researchers explored the link between income inequality and growth through the lens of the developmental stage of the economy. Kuznets [38] documented that the relationship between the two variables relies on the level of economic development of a country, meaning there is a differential relationship between income inequality and growth, with a positive relationship during the early stage of economic development and a negative relationship during the mature stage. This may be attributed to shifts of labor, from one sector to other, developed sectors. For example, when labor moves from the agricultural sector to other sectors of the economy, the per capita income of those individuals increases, as their skills are in demand in those sectors. Individuals who remain in the agricultural sector keep earning a low income, thus income inequality increases during this stage. As the economy develops, with labour continuing to move from agriculture to other sectors, individuals who remain in the agricultural sector will earn higher incomes due to the low supply of labour in that sector. Income inequality thus declines during this stage. Kuznets [38] describes the relationship as an inverted U-hypothesis, which advocates that inequality tends to increase during early stages of economic development and decrease during later stages. This argument is supported by Ahluwalia [7] , Robinson [54] and Gupta and Singh [31] .

The level of technological development

In addition to sectoral change, Galor and Tsiddon [29] , Helpman [33] and Aghion et al. [6] explored the link by connecting income inequality to the developmental stage of technology. During the early stages of technological development, innovative ideas in the economic sector result in increases in income inequality. This is due to the fact that new technology requires highly skilled labor and training, which raises wages in these sectors compared to those sectors which use old technology. As a result, employees in the new sector earn high per-capita incomes, while those working in the sector with old machines continue earning lower incomes. Therefore, income inequality increases during the early stages of technological improvements. However, as the economy moves to the more mature stage of technological development, income inequality decreases, the reason being that as more labour shifts to the sector using new technology, the incomes of those who remained in the sector with old technology also increase due to the low supply in labor in that sector. Therefore, the wage differential gap between them declines, leading to a decrease in income inequality.

The role of technology was probed further by researchers who focused on the Fourth Industrial Revolution (4IR). By implementing modern technologies, 4IR will lead to the continuing automation of traditional manufacturing and industrial processes. As Krueger [37] documented, improvements in technology widen the income inequality gap in the labor market between skilled and lowly skilled labour, because the income of highly skilled labor increases (as those individuals are in demand), while lowly skilled laborers continue earning low incomes. In similar vein, 4IR is skills-biased, which leads to a widening of the income inequality gap [1] . Based on this argument, technological improvements can be harmful to growth, due to concerns about growing inequality and unemployment.

Social-political unrest

Some studies argue that the rise of socio-political unrest, stemming from high income inequality, may dampen growth (see [13 , 14 , 62] ). In countries with extreme wealth and income inequality, there are high levels of social unrest that cause people to engage in strikes, criminality and other unproductive activities. This often results in wastage of government resources and disruptions that threaten the political stability of the country. It causes uncertainty in government and slows down productivity in the economy, while discouraging investment.

By contrast, high income inequality due to the rise of socio-political unrest can promote growth. To reduce the number of strikes, criminal activity, uncertainty and political unrest, politicians and leaders favor redistribution – from the rich to the poor – in the form of a transfer of payments. In turn, this creates a safety net for the population and government to restore society's trust in government. As a result, levels of uncertainty decline and investment increases, prompting an increase in the growth rate in the long run [13 , 14 , 62] . Similarly, Saint-Paul and Verdier [56] demonstrate that, in the presence of high income inequality, the median voter favors a transfer of payments by means of public expenditure, such as financing education. This, in turn, increases human capital for the poor to access education, thereby promoting growth.

The political economy

Political economy models demonstrate that high income inequality may hinder growth (see [9 , 13 , 48] ). The law and government play crucial roles in the economy, with government in charge of the redistribution of income and resources among the population. These models reveal that when the mean income is greater than that of the median voter, people support the redistribution of income and resources (from the rich to the poor). Redistribution takes place through a transfer of payments and public expenditure, such as the establishment of health facilities and the building of schools, among others. This kind of redistribution reduces growth in the long run, however, by discouraging innovation and investment, and causing low productivity [9 , 13 , 48] . In addition, when there is high income inequality, the population demands equal distribution. That sometimes results in riots and other unproductive activities which retard economic growth. Furthermore, factors such as lobbying and rent-seeking, which often occur during the political process, also discourage growth. This happens when those in the upper decile of income distribution prevent the redistribution of income and resources to the poor, resulting in a wastage of government funds and corruption, both of which hamper economic growth in the long run [11 , 46 , 48 , 50] .

The imperfection of credit markets

The imperfect credit markets model demonstrates that income inequality is negatively associated with growth through credit markets (see [4 , 12 , 30 , 51] ; Panniza, [46] ). In an imperfect credit market, a high degree of income inequality limits the poor from accessing credit. Asymmetric information – where the lender and borrower have limited information about each other – inhibits the ability to make well-informed decisions. This limits the ability to borrow and returns on investment. In addition, imperfect laws make it difficult for creditors to collect defaulted loans, because the law might protect the assets of the borrower from being repossessed as collateral. Such laws constrain the collection of debt, leading to the hard terms and conditions faced by potential creditors. This prohibits access to credit for some individuals, in particular the poor. Given that investment depends on how much income and how many assets an individual has, the poor (who only have income for basic necessities) are unable to afford investment opportunities with high returns (for instance, to invest in human capital or property, among others). For this reason, extremely high income inequality reduces investment opportunities, leading to declining growth in the long run.

Existing studies report that income inequality exerts a positive impact on economic growth through savings rates (see [3 , 13 , 17] ). According to these studies, savings are a function of income. As income earned increases, so the savings rate rises, and vice versa. In the presence of high income inequality, rich people earn high incomes which help them to save more, because their marginal propensity to save is relatively high. This increases the aggregate savings, leading to a rise in capital accumulation, thereby enhancing economic growth in the long run (see [3] , [17] , [42] , [53] , [66] ). Following on this argument, Shin [59] demonstrates that the redistribution of income and resources from rich to poor is harmful to growth. Such action reduces the income, wealth and other resources of the rich, leading to a decline in the marginal propensity to save. As a result, aggregate savings and investments decline.

Institutions

Several studies illustrate that income inequality inhibits growth through institutions (see [22 , 34 , 61] ). Institutions play a vital role in the wellbeing of a country, because they are the key drivers of economic growth and development in the long run [2 , 60 , 65] . The quality of institutions is important for distribution and growth outcomes. High income inequality creates fertile ground for bad institutions, and exacerbates inequality and inefficiency, which leads to low growth rates in the long run. In the case of high income inequality, political decisions tend to be biased towards enriching the already rich minority, at the expense of the poor. This results in poor policies, leading to a high level of inefficiency, wastage of state resources, social dissatisfaction and political instability. It further perpetuates inequality and inhibits growth in the long run [34 , 61] . Based on this argument, bad institutions tend to associate with extreme records of inequality, inefficiency and sluggish growth. By contrast, good institutions tend to associate with low inequality, productivity and economic growth.

The fertility rate

Income inequality has been found to negatively affect growth through differences in fertility (see [26] ). This study documented that a widening income inequality gap raises differences in fertility between the rich and the poor in a population. The low-income group usually have many children, and tend to invest less in their children's education due to a lack of financial resources. By contrast, those in the high-income group usually have fewer children and invest more in their education. Therefore, in the case of extreme income inequality, the high fertility differential has a negative impact on human capital, leading to a decline in economic growth.

Income inequality and economic growth: Empirical evidence

Given such theoretical ambiguity, the empirical findings on the relationship between income inequality and growth are also highly debatable. These findings can broadly be classified into four categories, namely negative, positive, inconclusive and no relationship.

Studies with negative results on the relationship between income inequality and economic growth

The earliest empirical studies examining the inequality–growth nexus were conducted in the 1990s, and employed the ordinary least squares (OLS) and two-stage least squares (2SLS) estimation techniques (see [9 , 49 , 50] ). Alesina and Rodrik [9] examined the relationship between distributive politics and economic growth in 46 countries, for the period 1960–1985. They found that higher income inequality was accompanied by low growth. Similarly, Persson and Tabellini [50] examined the impact of inequality on growth in 56 countries, for the period 1960–1985, and found that inequality exerted a negative impact on growth. Using similar estimating techniques, Perotti [49] analysed the relationship between income distribution, democratic institutions and growth in 67 countries, and found that countries with a low level of inequality tended to have high investments in human capital, which then led to economic growth.

Studies in the 2000s developed different estimation techniques to solve the problem at hand. For example, Panizza [46] employed the standard fixed effect (FE) and generalised method of moments (GMM) to reassess the relationship between income inequality and economic growth in the United States for the period 1940–1980. The results of that study documented that income inequality negatively affected economic growth. Another single-country study was conducted on China, where Wan et al. [64] investigated the short- and long-run relationship between inequality and economic growth during the period 1987–2001. By using three-stage least squares, they found that the relationship was nonlinear and negative for China. Recently, Iyke and Ho [35] studied income inequality and growth in Italy, from 1967–2012, using the autoregressive distributed lag (ARDL) estimation technique. Their study found that income inequality affected growth both in the short and long run. That is, income inequality slowed down growth in the country.

In multiple-country studies, Knowles [36] re-examined the relationship between inequality and growth in 40 countries using comparable data and OLS from 1960–1990. That investigation found a negative relationship between inequality and economic growth for the full sample. When the countries were divided according to the income level, he found a significant negative relationship in the low-income countries but an insignificant relationship in high- and middle-income countries. Malinen [41] investigated a sample comprising 60 countries (developed and developing economies) using the Gini index as a measure of income inequality. Panel cointegration methods were used, employing panel dynamic OLS and panel dynamic seemingly unrelated regression (SUR) to analyze the steady state correlation between income inequality and economic development. During the period under study, the findings revealed a negative steady-state correlation between income distribution and economic development. In addition, in developed countries, income inequality was associated with low economic growth in the long run. Another study focused on developed countries: Cingano [23] , for instance, examined the impact of income inequality and economic growth in OECD (Organisation for Economic Co-operation and Development) countries between 1980 and 2012. Employed GMM, the researcher found that in those countries income inequality negatively affected economic growth. Furthermore, the study confirmed human capital as the transmission channel through which income inequality affects growth. Research by Braun et al. [18] , tested the main prediction of their model with respect to the impact of income inequality on growth at different levels of financial development. By using pooled OLS, dynamic panel and instrumental variables (IV) estimations on 150 countries during the period between 1978 and 2012, they found that greater income inequality is associated with lower economic growth. In addition, they also found that such an effect is significantly attenuated when the level of financial development increases in economies. Another study by Royuela et al. [55] tested the income inequality-growth nexus for over 200 comparable regions in 15 OECD countries during 2003–2013. By using the similar estimation techniques of Bruan et al. [18] , they showed a general negative association between inequality and growth in OECD regions. Recently, Breunig and Majeed [19] re-investigated the impact of inequality and economic growth in 152 countries. The study used GMM for the period 1956 to 2011 and found that inequality had a negative effect on growth. They further found that when both poverty and inequality were considered, the negative impact of inequality on growth was concentrated on countries with high rates of poverty.

Studies with positive results on the relationship between income inequality and economic growth

A study which found a positive relationship is that of Partridge [47] , who investigated whether inequality benefited or hindered growth in the United States between 1960 and 1990. That study, which employed OLS, yielded the following results: first, during the period of the study, a positive relationship was found between inequality and economic growth. That is, American states with high inequality grew faster. Second, the study reported that the wellbeing of the median voter had a positive impact on growth. This implies that the unequal distribution of income and resources among the population encouraged economic activity and, in turn, grew the economy. In another single-country study, Rangel et al. [52] focused on growth and income inequality by investigating the linear correlation and inverted-U shape hypothesis in Brazil, from 1991–2000. They found that, in the long run, income inequality and growth tended to move together. The results also confirmed the existence of the inverted-U hypothesis between income inequality and economic growth.

Bhorat and Van der Westhuizen [16] investigated the relationship between economic growth, poverty and inequality in South Africa, for the period 1995–2005. The study employed a distribution-neutral measure, poverty inequality elasticity estimates, and the marginal proportional rate of substitution. During the period under study, the researchers found a shift in the distribution of income and resources during periods of growth, and hence income inequality tended to increase with increases in economic growth. Later, Shahbaz [58] and Majeed [40] both employed the ARDL technique to study the income inequality–growth nexus in Pakistan, with the first investigation spanning the years 1971–2005, and the second, 1975–2013. Both studies identified a positive correlation between income inequality and economic growth in Pakistan during the period under investigation. Majeed [40] further argued that because the poor population did not participate in the growth process, growth became unsustainable.

Studies on multiple countries also reported positive results. For example, Li and Zou [39] re-examined the relation between inequality and growth from 1947–1994 for a group of developed and developing countries. Using FE and RE methods and expanded data, they found that high income inequality resulted in an increase in economic growth. Later, Forbes [28] also re-assessed the inequality–growth relationship in 45 countries, from 1966–1995. With the use of FE and RE, Chamberlain's ᴫ matrix procedure and Arrelano and Bond's GMM, the findings showed that as income increased in the short to medium term, economic growth tended to increase. A recent study by Scholl and Klasen [57] revisited the inequality-growth relationship, paying special attention to the role of transition (post-Soviet) countries. The study was based on the specification used by Forbes [28] on a sample of 122 countries over the period of 1961–2012. By using FE, GMM and IV estimation techniques, they found a positive association between inequality and growth in the overall sample which was driven by transition countries.

Studies with inconclusive results on the relationship between income inequality and economic growth

A number of studies yielded inconclusive findings on the inequality–growth nexus. In particular, most reported that the relationship was positive in high-income countries and negative in the low-income countries. For example, Deininger and Squire [25] employed cross-country samples from 1960–1992 to analyse the influence of inequality (income and distribution of assets) on economic growth, and also studied the effect it exerts on reducing poverty. Using OLS and panel data, that study found that income inequality had a negative effect on future growth. In addition, Deininger and Squire [25] reported that high income inequality reduced the income of the poor and boosted the income of the rich. Barro [13] used 2SLS to study the inequality–growth relationship in a panel of countries for the period 1965–1995. The results showed that, in rich countries, inequality positively affected economic growth, while in poor countries it negatively affected growth during the period under study. This means that, for rich countries, as inequality increased, the economy (as measured by Gross Domestic Product [GDP] per capita) tended to increase as well, while in poor countries, the economy tended to decline as inequality increased.

Studies using GMM methods reached similar results. For example, Voitchovsky [63] analysed the link between income distribution and economic growth in 21 developing countries, from 1975–2000. The findings showed that income inequality had a positive effect on growth at the upper decile of income distribution, while inequality negatively affected growth at the lower decile. Similarly, Castelló-Climent [21] confirmed that the relationship between income and growth was positive in high-income countries and negative in low- and middle-income countries. That study examined the correlation between income and human capital and economic growth across countries during the period 1992–2000. The results further indicated that both income and human capital inequality constrained economic growth for low- and middle-income countries. However, in high-income countries, income and human capital inequality encouraged economic growth during the period under study. In yet another investigation, Fawaz et al. [27] studied the income inequality–growth nexus, focusing on its link to credit constraints in high- and low-income developing countries from 1960–2010. The study found similar results, namely that in low-income developing countries, income inequality is negatively related to economic growth. For high-income developing countries, income inequality was positively related to economic growth.

Halter et al. [32] reported that this relationship changed over time, having studied the relationship across countries from 1965–2005, using GMM. The findings showed that, in the short run, high inequality encouraged economic growth, but over the long run, high inequality slowed down the economy and impeded growth. Likewise, Ostry et al. [45] investigated the link between redistribution, inequality and growth in various countries, and found that net inequality was positively correlated to economic performance during the early stage of economic development, but turned negative during the mature stage. Research by Brueckner and Lederman [20] studied the relationship between inequality and GDP per capita growth. Using panel data from 1970 to 2010, the findings documented that in low income countries transitional growth was positively affected by higher income inequality while such effect turned negative in high income countries.

Studies with evidence of no relationship between income inequality and economic growth

Some studies reported no relationship between income inequality and economic growth. For example, research by Niyimbanira [44] focused on how economic growth affected income inequality from 1996–2014. That study employed the FE method and the pooled regression model, using data from 18 municipalities across the provinces of South Africa. The findings confirmed that economic growth reduced poverty, but had no effect on income inequality, which implies that there was no relationship between income inequality and economic growth. Benos and Karagiannis [15] examined the relationship between top income inequality and growth under the influence of physical and human capital accumulation in the U.S. By using 2SLS and GMM on the annual panel of U.S. state-level data during 1929 to 2013, they concluded that changes in inequality do not have an impact on growth. Table 1 shows the summary of empirical studies discussed in this section.

Summary of empirical studies on the association between income inequality and economic growth

Note: - denotes negative; + denotes positive; 0 denotes no relationship

Methodology

As we have discussed in the previous section, the empirical findings on income inequality and growth are highly inconclusive. In this section, by providing a critical survey on methodology issues employed in the prior studies, we offer possible explanations on the disparity found in the empirical findings, particularly on multiple-countries studies. The early multiple-countries studies [9 , 49 , 50] in general reached a consensus on the negative impact of inequality on growth. Although they used different measures of inequality and samples, they all employed the Ordinary Least Squares (OLS) and Two-Stage Least Squares (2SLS) estimation techniques on cross-section data to estimate the coefficient on the inequality variable.

By the late 1990s, however, the general consensus on the negative relationship between income inequality and growth was challenged by concerns over data quality and the methodological procedures used (see Neves and |Silva, [43] ). With regard to the data quality, some studies argued that the dataset used in the previous studies, which lacked comparability due to the use of different income definitions (gross income versus expenditures) can lead to different results (see [10 , 36] ). According to Knowles [36] , European countries, the U.S. and most of the Latin American countries use gross income data whereas most of the African and Asian countries use expenditure data. Since expenditure is more equally distributed than gross income, such difference in income distribution may lead to a difference in the final results.

Concerning the methodological procedures, there has been a shift on the usage of panel data instead of cross-sectional data in the later studies. Forbes [28] argues that the use of panel data is desirable as it can specifically estimate how a change in a country's level of inequality within a given country will affect growth in that country. In addition, panel data can remove bias from the correlation between time-variant, observable country characteristics and the explanatory variables by controlling for differences in these characteristics. Due to these considerations, many studies started to use panel data (see [13 , 28 , 39] ; among others). However, the use of panel data in the studies may lead to more diverse results. One of the possible explanations is the diversity of estimators employed in the panel studies. While most of the cross-section studies use OLS, panel studies use a wide variety of estimators such as fixed effects, random effects, GMM, etc. Given that these estimators have different underlying assumptions, they are likely to produce different results among the panel studies [43] . Another possible explanation is that, unlike the cross-section data, panel data controls for time-variant, observable country characteristics. Given that the impact of inequality on growth tends to differ across countries and regions, the inter-continental variation contribute a substantial part of the effect. Therefore, the usage of panel data analysis may lead to different results when different samples are used in the studies. With the wider usage of various panel data estimation techniques in the later studies, it is not surprising that we found more diverse results in the inequality-growth literature.

Based on the above considerations, researchers should be more cautious when identifying a general global pattern regarding the inequality-growth relationship. Instead, we propose that more emphasis should be placed on identifying the inequality-growth relationship on a national or regional level. Such an approach will provide a better understanding of the inequality-growth process on the study area by overcoming data comparability constraints and possible methodological challenges.

This paper presented a comprehensive literature review of the relationship between income inequality and economic growth. In the theoretical literature, various transmission mechanisms were identified in which income inequality is linked to economic growth, namely the level of economic development, the level of technological development, social-political unrest, the political economy, the imperfection of credit markets, the savings rate, institutions, and the fertility rate. Based on these models, we found that the relationship between income inequality and growth can be negative, positive or inconclusive. For example, based on the level of economic and technological development, the relationship between inequality and growth is positive and becomes negative as the level of development progresses. Inconclusive results were reported by the social-political unrest model, showing that the rise in socio-political unrest stemming from high-income inequality could either dampen or promote growth. In addition, theories on the political economy, the imperfection of credit markets, institutions and the fertility rate, reported that income inequality was negatively related to growth. The only theory which supported the positive relationship between income inequality and growth was the theory of savings rates.

On the empirical front, we found that numerous studies joined the debate by testing the relationship between income inequality and economic growth. Some found a positive relationship, while others identified a negative impact. Some studies yielded inconclusive findings. In particular, most found that the relationship was positive in high-income countries and negative in low-income countries. Several studies documented no relationship between income inequality and economic growth. In the methodology section, we provided a critical survey on methodology issues employed in the prior studies. We argued that the varying results obtained by these studies can be attributed to empirical aspects such as the data comparability and methodological procedures used. We, therefore, suggest that future studies should place more emphasis on identifying the inequality-growth relationship on a national or regional level to better understand the inequality-growth process on the study area. In addition, we conjecture that as the study countries and time span differed in the empirical studies, the impacts of the various theoretical channels we identified previously could also play a uniquely important role in affecting the relationship of the inequality–growth nexus in those studies. It would be prudent for future studies to apply the theoretical models to provide an in-depth analysis of the existing empirical findings. Such findings, with reference to the social, political and economic structure, would provide more relevant policy recommendations to the countries under study.

Declaration of Competing Interest

The authors of this paper certify that there is no financial or personal interest that influenced the presentation of the paper.

Sustainable manufacturing intelligence: pathways for high-quality and energy efficient economic growth

• Published: 13 April 2024
• Volume 57 , article number  100 , ( 2024 )

Cite this article

• Lei Xia 1 ,
• Qingjiang Han 2 &
• Shui Yu 1  

Manufacturing intelligence is an important starting point to achieve high-quality economic growth. Based on the non-radial and non-angular DEA-SBM model, combined with the Malmquist index method, this paper calculates the total factor productivity and green total factor productivity of 286 prefecture-level cities in China, which are used as the indicators of high-quality economic growth. At the same time, the proxy variables of manufacturing intelligence are further constructed, and the impact of intelligent manufacturing on high-quality and energy-efficient economic growth is empirically analyzed. It is found that intelligent manufacturing significantly promotes China’s high-quality and energy-efficient economic growth. Productivity is further decomposed into specific indicators such as technical efficiency, technological progress, pure technical efficiency, pure technical progress, scale efficiency and scale technology, and the mechanism of intelligent manufacturing is analyzed from multiple angles. The research finds that manufacturing intelligence improves the total factor productivity through technological progress effect. Green total factor productivity has been improved through the improvement of technical efficiency effect, but the development of intelligence has brought new challenges to China's labor market. In order to further develop the potential of intelligence, it is necessary to further improve the enterprises’ scale efficiency, while increasing the research and development of energy technology.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price includes VAT (Russian Federation)

Instant access to the full article PDF.

Rent this article via DeepDyve

Institutional subscriptions

Acemoglu D, Restrepo P (2017) Robots and jobs: evidence from US labor markets. NBER Working Paper no 23285

Acemoglu D, Restrepo P (2018) Artificial intelligence, automation and work. Social Science Electronic Publishing

Book   Google Scholar  

Acemoglu D, Restrepo P (2020) Robots and jobs: evidence from US labor markets. J Polit Econ 128(6):2188–2244

Article   Google Scholar  

Anderson MC, Banker RD, Janakiraman SN (2003) Are selling, general, and administrative costs “sticky.” J Account Res 41(1):47–63

Andrews D, Criscuolo C, Gal PN (2016) The best versus the rest: the global productivity slowdown, divergence across firms and the role of public policy. Oecd Productivity Working Papers

Ang JB, Madsen JB, Islam MR (2011) The effects of human capital composition on technological convergence. J Macroecon 33(3):465–476

Autor DH, Salomons A (2017) Robocalypse now:does productivity growth threaten employment? ECB Sintra Forum on Central Banking papers

Autor DH, Levy F, Murnane RJ (2003) The skill content of recent technological change: an empirical exploration. Q J Econ 118(4):1279–1333

Autor D, Dorn D, Lawrence F, Patterson C, Van Reenen J (2017) The fall of the labor share and the rise of superstar firms. NBER Working Paper 23396, Cambridge, MA

Baumol W (1967) Macroeconomics of unbalanced growth: the anatomy of urban crisis. Am Econ Rev 57:415–426

Google Scholar  

Binder CR, Mühlemeier S, Wyss R (2017) An Indicatorbased approach for analyzing the resilience of transitions for energy regions. Part I: theoretical and conceptual considerations. Energies 10(36):1–18

Brynjolfsson E, Rock D, Syverson C (2019) Artificial intelligence and the modern productivity paradox: a clash of expectations and statistics. Social Science Electronic Publishing

Byrne DM, Fernald JG, Reinsdorf MB (2016) Does the United States have a productivity slowdown or a measurement problem?. Brookings Papers on Economic Activity.

Cai Y, Chen N (2019) Artificial Intelligence and high-quality growth and employment under the new technological revolution. J Quant. Tech. Econ. 36(05):3–22

Dai J, Ahmed Z, Pata UK, Ahmad M (2023) Achieving SDG-13 in the era of conflicts: the roles of economic growth and government stability. Eval Rev 47:1168–1192

Dai J, Hiung EYT, Destek MA, Ahmed Z (2024) Green policymaking in top emitters: assessing the consequences of external conflicts, trade globalization, and mineral resources on sustainable development. Int J Sustain Dev World Ecol, 1–15.

Dauth W, Findeisen S, Sidekum J (2017) German robots-the impact of industrial robots on workers. CEPR Discussion Papers, No DP12306

Fan S, Zhang X (2004) Infrastructure and regional economics development in rural China. China Econ Rev 15:203–214

Frankel JA, Romer DH (1999) Does trade cause growth? Am Econ Rev 89(3):379–399

Frey CB, Osborne MA (2017) The future of employment: how susceptible are jobs to computerisation? Technol Forecast Soc Change 114:254–280

Gordon RJ (2018) Why Has economic growth slowed when innovation appears to be accelerating?. Nber Working Papers

Graetz G, Michaels G (2015) Robots at Work. CEP Discussion Paper no 1335

Grubler A (2004) Transitions in energy use. Encycl Energy 6:163–177

Hanson R (2001) Economic growth given machine intelligence, Technical report, University of California Berkeley

Hunjra AI, Bouri E, Azam M, Azam RI, Dai J (2024) Economic growth and environmental sustainability in developing economies. Res Int Bus Finance 70(1):102341

Irandoust M (2016) The renewable energy-growth nexus with carbon emissions and technological innovation: evidence from the Nordic countries. Ecol Indic 69:118–125

Javorcik BS (2004) Does foreign direct investment increase the productivity of domestic firms? In search of spillovers through backward linkages. Am Econ Rev 94(3):605–627

Jones CI (2002) Introduction to economic growth. J Econ Theory 147(2):545–550

Kou J, Sun F (2020) An analysis on the promotion and prospect of scientific and technological innovation for China’s energy transformation. Sci Technol Manag Res 40(16):244–250

Li C (2015) Review on the “export-productivity paradox” of Chinese enterprises. World Econ 038(005):148–175

Li L, Bao Y, Liu J (2020) Study on the impact of Intellectualization on the total factor productivity of China’s manufacturing industry. Stud Sci Sci 252(04):36–45

Li L, Zhou S, Xu W, Dai J (2024) Green innovation's impact on corporate financing: new insights from BRICS economies. Finance Res Lett 62(Part B):105172

Lin JY (2015) The Washington Consensus revisited: a new structural economics perspective. J Econ Policy Reform 18(2):96–113

Lin J, Monga C (2011) Growth identification and facilitation?: The role of the state in the dynamics of structural change. Dev Policy Rev 29(3):264–290

Liu H, Khan I, Zakari A, Alharthi M (2022) Roles of trilemma in the world energy sector and transition towards sustainable energy: a study of economic growth and the environment. Energy Policy 170:113238

Lv L, Deng Z, Liu T, Li Z, Liu W (2020) Intelligent technology in grinding process driven by data: a review. J Manuf Process 58:1039–1051

Lv Jie Du, Chuanwen LY (2017) Will the application of industrial robots force the transformation of labor structure in a country’s manufacturing industry? Based on the experience analysis of 22 countries from 1990 to 2015. Sci Technol Manag Res 22:32–41

Murshed M, Mahmood H, Ahmad P, Rehman A, Alam S (2022) Pathways to Argentina’s 2050 carbon-neutrality agenda: the roles of renewable energy transition and trade globalization. Environ Sci Pollut Res 29(20):29949–29966

Reyes-Mercado P, Rajagopa L (2017) Adoption of renewable energy technologies in Mexico: the role of cognitive factors and innovation attributes. Int J Energy Sect Manag 11(4):626–649

Song X, Zuo MH (2019) Input, labor supply and labor productivity of industrial robots. Reform 9:45–54

Tabrizian S (2019) Technological innovation to achieve sustainable development: renewable energy technologies diffusion in developing countries. Sustain Dev 27(3):537–544

Wang Y, Dong W (2020) How is the rise of robots affecting China’s Labour market?–Evidence from listed manufacturing companies. Econ Res 55(10):159–175

Wang Z, Huang Y, Ankrah V, Dai J (2023) Greening the knowledge-based economies: Harnessing natural resources and innovation in information and communication technologies for green growth. Resour Policy 86:104181

Wang B, Wang J, Dong K (2024) How does artificial intelligence affect high-quality energy development? Achieving a clean energy transition society. Energy Policy 186:114010

Xu R, Pata UK, Dai J (2023) Sustainable growth through green electricity transition and environmental regulations: do risks associated with corruption and bureaucracy matter? Politická ekonomie. In press

Yang G, Hou Y (2020) The use of industrial robots, technological upgrading and economic growth. China Industrial Econ 391(10):140–158

Yao S, Zhang Z (2001) Regional growth in China under economic reforms. J Dev Stud 38(2):167–186

Yuan C, Xiao T, Geng C, Sheng Y (2021) Digital transformation and division of labor between enterprises: Vertical specialization or vertical integration. China Industrial Econ 9:137–155

Zeira J (1998) Workers, machines, and economic growth. Quart J Econ 113(4):1091–1117

Download references

Acknowledgements

This work was sponsored by Humanities and Social Sciences Research Fund of the Ministry of Education(21YJC790041); National Natural Science Foundation of China (72363017); Science and Technology Research Project of Jiangxi Provincial Department of Education(GJJ210514); The 71st China Postdoctoral Science Foundation project (306895).

Author information

Authors and affiliations.

School of Economics, Wuhan Polytechnic University, Wuhan, 430023, China

Lei Xia & Shui Yu

School of Applied Economics, Jiangxi University of Finance and Economics, Nanchang, 330013, China

Qingjiang Han

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Qingjiang Han .

Ethics declarations

Conflict of interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Xia, L., Han, Q. & Yu, S. Sustainable manufacturing intelligence: pathways for high-quality and energy efficient economic growth. Econ Change Restruct 57 , 100 (2024). https://doi.org/10.1007/s10644-024-09692-z

Download citation

Received : 27 December 2023

Accepted : 19 March 2024

Published : 13 April 2024

DOI : https://doi.org/10.1007/s10644-024-09692-z

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Manufacturing intelligence
• High-quality growth
• Energy efficient
• Total factor productivity
• Green total factor productivity

Advertisement

• Find a journal
• Publish with us
• Track your research

• Reference Manager
• Simple TEXT file

People also looked at

Original research article, impact of industrial policy on urban green innovation: empirical evidence of china’s national high-tech zones based on double machine learning.

• College of Economics and Management, Taiyuan University of Technology, Taiyuan, China

Effective industrial policies need to be implemented, particularly aligning with environmental protection goals to drive the high-quality growth of China’s economy in the new era. Setting up national high-tech zones falls under the purview of both regional and industrial policies. Using panel data from 163 prefecture-level cities in China from 2007 to 2019, this paper empirically analyzes the impact of national high-tech zones on the level of urban green innovation and its underlying mechanisms. It utilizes the national high-tech zones as a quasi-natural experiment and employs a double machine learning model. The study findings reveal that the policy for national high-tech zones greatly enhances urban green innovation. This conclusion remains consistent even after adjusting the measurement method, empirical samples, and controlling for other policy interferences. The findings from the heterogeneity analysis reveal that the impact of the national high-tech zone policy on green innovation exhibits significant regional heterogeneity, with a particularly significant effect in the central and western regions. Among cities, there is a notable push for green innovation levels in second-tier, third-tier, and fourth-tier cities. The moderating effect results indicate that, at the current stage of development, transportation infrastructure primarily exerts a negative moderating effect on how the national high-tech zone policy impacts the level of urban green innovation. This research provides robust empirical evidence for informing the optimization of the industrial policy of China and the establishment of a future ecological civilization system.

1 Introduction

The Chinese economy currently focuses on high-quality development rather than quick growth. The traditional demographic and resource advantages gradually diminish, making the earlier crude development model reliant on excessive resource input and consumption unsustainable. Simultaneously, resource impoverishment, environmental pollution, and carbon emissions are growing more severe ( Wang F. et al., 2022 ). Consequently, pursuing a mutually beneficial equilibrium between the economy and the environment has emerged as a critical concern in China’s economic growth. Green innovation, the integration of innovation with sustainability development ideas, is progressively gaining significance within the framework of reshaping China’s economic development strategy and addressing the challenges associated with resource and environmental limitations. In light of the present circumstances, and with the objectives outlined in the “3060 Plan” for carbon peak and carbon neutral, the pursuit of a green and innovative development trajectory, emphasizing heightened innovation alongside environ-mental preservation, has emerged as a pivotal concern within the context of China’s contemporary economic progress.

Industrial policy is pivotal in government intervention within market-driven resource allocation and correcting structural disparities. The government orchestrates this initiative to bolster industrial expansion and operational effectiveness. In contrast to Western industrial policies, those in China are predominantly crafted within the administrative framework and promulgated through administrative regulations. Over an extended period, numerous industrial policies have been devised in response to regional disparities in industrial development. These policies aim to identify new growth opportunities in diverse regions, focusing on optimizing and upgrading industrial structures. These strategies have been implemented at various administrative levels, from the central government to local authorities ( Sun and Sun, 2015 ). As a distinctive regional economic policy in China, the national high-tech zone represents one of the foremost supportive measures a city can acquire at the national level. Its crucial role involves facilitating the dissemination and advancement of regional economic growth. Over more than three decades, it has evolved into the primary platform through which China executes its strategy of concentrating on high-tech industries and fostering development driven by innovation. Concurrently, the national high-tech zone, operating as a geographically focused policy customized for a specific region ( Cao, 2019 ), enhances the precision of policy support for the industries under its purview, covering a more limited range of municipalities, counties, and regions. Contrasting with conventional regional industrial policies, the industry-focused policy within national high-tech zones prioritizes comprehensive resource allocation advice and economic foundations to maximize synergy and promote the long-term sustainable growth of the regional economy, and this represents a significant paradigm shift in location-based policies within the framework of carrying out the new development idea. Its inception embodies a combination of central authorization, high-level strategic planning, local grassroots decision-making, and innovative system development. In recent years, driven by the objective of dual carbon, national high-tech have proactively promoted environmentally friendly innovation. Nevertheless, given the proliferation of new industrial policies and the escalating complexity of the policy framework, has the setting up of national high-tech zones genuinely elevated the level of urban green innovation in contrast to conventional regional industrial policies? What are the underlying mechanisms? Simultaneously, concerning the variations among different cities, have the industrial policy tools within the national high-tech zones been employed judiciously and adaptable? What are the concrete practical outcomes? Investigating these matters has emerged as a significant subject requiring resolution by government, industry and academia.

2 Literature review and research hypothesis

2.1 literature review.

When considering industrial policy, the setting up national high-tech zones embodies the intersection of regional and industrial policies. Domestic and international academic research concerning setting up national high-tech zones primarily centers on economic activities and innovation. Notably, the economic impact of national high-tech zones encompasses a wide range of factors, including their influence on total factor productivity ( Tan and Zhang, 2018 ; Wang and Liu, 2023 ), foreign trade ( Alder et al., 2016 ), industrial structure upgrades ( Yuan and Zhu, 2018 ), and economic growth ( Liu and Zhao, 2015 ; Huang and Fernández-Maldonado, 2016 ; Wang Z. et al., 2022 ). Regarding innovation, numerous researchers have confirmed the positive effects of national high-tech zones on company innovation ( Vásquez-Urriago et al., 2014 ; Díez-Vial and Fernández-Olmos, 2017 ; Wang and Xu, 2020 ); Nevertheless, a few scholars have disagreed on this matter ( Hong et al., 2016 ; Sosnovskikh, 2017 ). In general, the consensus among scholars is that setting up high-tech national zones fosters regional innovation significantly. This consensus is supported by various aspects of innovation, including innovation efficiency ( Park and Lee, 2004 ; Chandrashekar and Bala Subrahmanya, 2017 ), agglomeration effect ( De Beule and Van Beveren, 2012 ), innovation capability ( Yang and Guo, 2020 ), among other relevant dimensions. The existing literature predominantly delves into the correlation between the setting up of national high-tech zones, innovation, and economic significance. However, the rise of digital economic developments, notably industrial digitization, has accentuated the limitations of the traditional innovation paradigm. These shortcomings, such as the inadequate exploration of the social importance and sustainability of innovation, have become apparent in recent years. As the primary driver of sustainable development, green innovation represents a potent avenue for achieving economic benefits and environmental value ( Weber et al., 2014 ). Its distinctiveness from other innovation forms lies in its potential to facilitate the transformation of development modes, reshape economic structures, and address pollution prevention and control challenges. However, in the context of green innovation, based on the double-difference approach, Wang et al. (2020) has pointed out that national high-tech zones enhance the effectiveness of urban green innovation, but this is only significant in the eastern region.

Furthermore, scholars have also explored the mechanisms underlying the innovation effects of national high-tech. For example, Cattapan et al. (2012) focused on science parks in Italy. They found that green innovation represents a potent avenue for achieving economic benefits as the primary driver of sustainable development, and environmental value technology transfer services positively influence product innovation. Albahari et al. (2017) confirmed that higher education institutions’ involvement in advancing corporate innovation within technology and science parks has a beneficial moderating effect. Using the moderating effect of spatial agglomeration as a basis, Li WH. et al. (2022) found that industrial agglomeration has a significantly unfavorable moderating influence on the effectiveness of performance transformation in national high-tech zones. Multiple studies have examined the national high-tech zone industrial policy’s regulatory framework and urban innovation. However, in the age of rapidly expanding new infrastructure, infrastructure construction is concentrated on information technologies like blockchain, big data, cloud computing, artificial intelligence, and the Internet; Further research is needed to explore whether traditional infrastructure, particularly transportation infrastructure, can promote urban green innovation. Transportation infrastructure has consistently been vital in fostering economic expansion, integrating regional resources, and facilitating coordinated development ( Behrens et al., 2007 ; Zhang et al., 2018 ; Pokharel et al., 2021 ). Therefore, it is necessary to investigate whether transportation infrastructure can continue encouraging innovative urban green practices in the digital economy.

In summary, the existing literature has extensively examined the influence of national high-tech zones on economic growth and innovation from various levels and perspectives, establishing a solid foundation and offering valuable research insights for this study. Nonetheless, previous studies frequently overlooked the impact of national high-tech zones on urban green innovation levels, and a subsequent series of work in this paper aims to address this issue. Further exploration and expansion are needed to understand the industrial policy framework’s strategy for relating national high-tech zones to urban green innovation. Furthermore, there is a need for further improvement and refinement of the research model and methodology. Based on these, this paper aims to discuss the industrial policy effects of national high-tech zones from the perspective of urban green innovation to enrich and expand the existing research.

In contrast to earlier research, the marginal contribution of this paper is organized into three dimensions: 1) Most scholars have primarily focused on the effects of national high-tech zones on economic activity and innovation, with less emphasis on green innovation and rare studies according to the level of green innovation perspective. The study on national high-tech zones as an industrial policy that has already been done is enhanced by this work. 2) Regarding the research methodology, the Double Machine Learning (DML) approach is used to evaluate the policy effects of national high-tech zones, leveraging the advantages of machine learning algorithms for high-dimensional and non-parametric prediction. This approach circumvents the problems of model setting bias and the “curse of dimensionality” encountered in traditional econometric models ( Chernozhukov et al., 2018 ), enhancing the credibility of the research findings. 3) By introducing transportation infrastructure as a moderator variable, this study investigates the underlying mechanism of national high-tech zones on urban green innovation, offering suggestions for maximizing the influence of these zones on policy.

2.2 Theoretical analysis and hypotheses

2.2.1 national high-tech zones’ industrial policies and urban green innovation.

As one of the ways to land industrial policies at the national level, national high-tech zones serve as effective driving forces for enhancing China’s ability to innovate regionally and its contribution to economic growth ( Xu et al., 2022 ). Green innovation is a novel form of innovation activity that harmoniously balances the competing goals of environmental preservation and technological advancement, facilitating the superior expansion of the economy by alleviating the strain on resources and the environment ( Li, 2015 ). National high-tech zones mainly impact urban green innovation through three main aspects. Firstly, based on innovation compensation effects, national high-tech zones, established based on the government’s strategic planning, receive special treatment in areas such as land, taxation, financing, credit, and more, serving as pioneering special zones and experimental fields established by the government to promote high-quality regional development. When the government offers R&D subsidies to enterprises engaged in green innovation activities within the zones, enterprises are inclined to respond positively to the government’s policy support and enhance their level of green innovation as a means of seeking external legitimacy ( Fang et al., 2021 ), thereby contributing to the advancement of urban green innovation. Secondly, based on the industrial restructuring effect, strict regulation of businesses with high emissions, high energy consumption, and high pollution levels is another aspect of implementing the national high-tech zone program. Consequently, businesses with significant emissions and energy consumption are required to optimize their industrial structure to access various benefits within the park, resulting in the gradual transformation and upgrading of high-energy-consumption industries towards green practices, thereby further contributing to regional green innovation. Based on Porter’s hypothesis, the green and low-carbon requirements of the park policy increase the production costs for polluting industries, prompting polluting enterprises to upgrade their existing technology and adopt green innovation practices. Lastly, based on the theory of industrial agglomeration, the national high-tech zones’ industrial policy facilitates the concentration of innovative talents to a certain extent, resulting in intensified competition in the green innovation market. Increased competition fosters the sharing of knowledge, technology, and talent, stimulating a market environment where the survival of the fittest prevails ( Melitz and Ottaviano, 2008 ). These increase the effectiveness of urban green innovation, helping to propel urban green innovation forward. Furthermore, the infrastructure development within the national high-tech zones establishes a favorable physical environment for enterprises to engage in creative endeavors. Also, it enables the influx of high-quality innovation capital from foreign sources, complementing the inherent characteristics of national high-tech zones that attract such capital and concentrate green innovation resources, ultimately resulting in both environmental and economic benefits. Based on the above analysis, Hypothesis 1 is proposed:

Hypothesis 1. Implementing industrial policies in national high-tech zones enhances levels of urban green innovation.

2.2.2 Heterogeneity analysis

Given the variations in economic foundations, industrial statuses, and population distributions across different regions, development strategies in different regions are also influenced by these variations ( Chen and Zheng, 2008 ). Theoretically, when using administrative boundaries or geographic locations as benchmarks, the impact of national high-tech zone industrial policy on urban green innovation should be achieved through strategies like aligning with the region’s existing industrial structure. Compared to the western and central regions, the eastern region exhibits more incredible innovation and dynamism due to advantages such as a developed economy, good infrastructure, advanced management concepts, and technologies, combined with a relatively high initial level of green innovation factor endowment. Considering the diminishing marginal effect principle of green innovation, the industrial policy implementation in national high-tech zones favors an “icing on the cake” approach in the eastern region, contrasting with a “send carbon in the snow” approach in the central and western regions. In other words, the economic benefits of national high-tech zones for promoting urban green innovation may need to be more robust than their impact on the central and western regions. Literature confirms that establishing national high-tech zones yields a more beneficial technology agglomeration effect in the less developed central and western regions ( Liu and Zhao, 2015 ), leading to a more substantial impact on enhancing the level of urban green innovation.

Moreover, local governments consider economic development, industrial structure, and infrastructure levels when establishing national high-tech zones. These factors serve as the foundation for regional classification to address variations in regional quality and to compensate for gaps in theoretical research on the link between national high-tech zone industrial policy implementation and urban green innovation. Consequently, the execution of industrial policies in national high-tech zones relies on other vital factors influencing urban green innovation. Significant variations exist in economic development and infrastructure levels among cities of different grades ( Luo and Wang, 2023 ). Generally, cities with higher rankings exhibit strong economic growth and infrastructure, contrasting those with lower rankings. Consequently, the effect of establishing a national high-tech zone on green innovation may vary across different city grades. Thus, considering the disparities across city rankings, we delve deeper into identifying the underlying reasons for regional diversity in the green innovation outcomes of industrial policies implemented in national high-tech zones based on city grades. Based on the above analysis, Hypothesis 2 is proposed:

Hypothesis 2. There is regional heterogeneity and city-level heterogeneity in the impact of national high-tech zone policies on the level of urban green innovation.

2.2.3 The moderating effect of transportation infrastructure

Implementing industrial policies and facilitating the flow of innovation factors are closely intertwined with the role of transport infrastructure as carriers and linkages. Generally, enhanced transportation infrastructure facilitates the absorption of local factors and improves resource allocation efficiency, thereby influencing the spatial redistribution of production factors like labor, resources, and technology across cities. Enhanced transportation infrastructure fosters the development of more robust and advanced innovation networks ( Fritsch and Slavtchev, 2011 ). Banister and Berechman (2001) highlighted that transportation infrastructure exhibits network properties that are fundamental to its agglomeration or diffusion effects. From this perspective, robust infrastructure impacts various economic activities, including interregional labor mobility, factor agglomeration, and knowledge exchange among firms, thereby expediting the spillover effects of green technological innovations ( Yu et al., 2013 ). In turn, this could positively moderate the influence of national hi-tech zone policies on green innovation. On the other hand, while transportation infrastructure facilitates the growth of national high-tech zone policies, it also brings negative impacts, including high pollution, emissions, and ecological landscape fragmentation. Improving transportation infrastructure can also lead to the “relative congestion effect” in national high-tech zones. This phenomenon, observed in specific regions, refers to the excessive concentration of similar enterprises across different links of the same industrial chain, which exacerbates the competition for innovation resources among enterprises, making it challenging for enterprises in the region to allocate their limited innovation resources to technological research and development activities ( Li et al., 2015 ). As a result, there needs to be a higher green innovation level. Therefore, the impact of transportation infrastructure in the current stage of development will be more complex. When the level of transport infrastructure is moderate, adequate transport infrastructure supports the promotion of urban green innovation through national high-tech zone policies. However, the impact of transport infrastructure regulation may be harmful. Based on the above analysis, Hypothesis 3 is proposed:

Hypothesis 3. Transportation infrastructure moderates the relationship between national high-tech zones and levels of urban green invention.

3 Research design

3.1 model setting.

This research explores the impact of industrial policies of national high-tech zones on the level of urban green innovation. Many related studies utilize traditional causal inference models to assess the impact of these policies. However, these models have several limitations in their application. For instance, the commonly used double-difference model in the parallel trend test has stringent requirements for the sample data. Although the synthetic control approach can create a virtual control group that meets parallel trends’ needs, it is limited to addressing the ‘one-to-many’ problem and requires excluding groups with extreme values. The selection of matching variables in propensity score matching is subjective, among other limitations ( Zhang and Li, 2023 ). To address the limitations of conventional causal inference models, scholars have started to explore applying machine learning to infer causality ( Chernozhukov et al., 2018 ; Knittel and Stolper, 2021 ). Machine learning algorithms excel at an impartial assessment of the effect on the intended target variable for making accurate predictions.

In contrast to traditional machine learning algorithms, the formal proposal of DML was made in 2018 ( Chernozhukov et al., 2018 ). This approach offers a more robust approach to causal inference by mitigating bias through the incorporation of residual modeling. Currently, some scholars utilize DML to assess causality in economic phenomena. For instance, Hull and Grodecka-Messi (2022) examined the effects of local taxation, crime, education, and public services on migration using DML in the context of Swedish cities between 2010 and 2016. These existing research findings serve as valuable references for this study. Compared to traditional causal inference models, DML offers distinct advantages in variable selection and model estimation ( Zhang and Li, 2023 ). However, in promoting urban green innovation in China, there is a high probability of non-linear relationships between variables, and the traditional linear regression model may lead to bias and errors. Moreover, the double machine learning model can effectively avoid problems such as setting bias. Based on this, the present study employs a DML model to evaluate the policy implications of establishing a national high-tech zone.

3.1.1 Double machine learning framework

Prior to applying the DML algorithm, this paper refers to the practice of Chernozhukov et al. (2018) to construct a partially linear DML model, as depicted in Eq. 1 below:

where i represents the city, t represents the year, and l n G I i t represents the explained variable, which in this paper is the green innovation level of the city. Z o n e i t represents the disposition variable, which in this case is a national high-tech zone’s policy variable. It takes a value of 1 after the implementation of the pilot and 0 otherwise. θ 0 is the disposal factor that is the focus of this paper. X i t represents the set of high-dimensional control variables. Machine learning algorithms are utilized to estimate the specific form of g ^ X i t , whereas U i t , which has a conditional mean of 0, stands for the error term. n represents the sample size. Direct estimation of Eq. 1 provides an estimate for the coefficient of dispositions.

We can further explore the estimation bias by combining Eqs 1 , 2 as depicted in Eq. ( 3 ) below:

where a = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t U i t , by a normal distribution having 0 as the mean, b = 1 n ∑ i ∈ I , t ∈ T   Z o n e i t 2 − 1 1 n ∑ i ∈ I , t ∈ T   Z o n e i t g X i t − g ^ X i t . It is important to note that DML utilizes machine learning and a regularization algorithm to estimate a specific functional form g ^ X i t . The introduction of “canonical bias” is inevitable as it prevents the estimates from having excessive variance while maintaining their unbiasedness. Specifically, the convergence of g ^ X i t to g X i t , n −φg > n −1/2 , as n tends to infinity, b also tends to infinity, θ ^ 0 is difficult to converge to θ 0 . To expedite convergence and ensure unbiasedness of the disposal coefficient estimates with small samples, an auxiliary regression is constructed as follows:

where m X i t represents the disposition variable’s regression function on the high-dimensional control variable, this function also requires estimation using a machine learning algorithm in the specific form of m ^ X i t . Additionally, V i t represents the error term with a 0 conditional mean.

3.1.2 The test of the mediating effect within the DML framework

This study investigates how the national high-tech zone industrial policy influences the urban green innovation. It incorporates moderating variables within the DML framework, drawing on the testing procedure outlined by Jiang (2022) , and integrates it with the practice of He et al. (2022) , as outlined below:

Equation 5 is based on Eq. 1 with the addition of variables l n t r a i t and Z o n e i t * l n t r a i t .Where l n t r a i t represents the moderating variable, which in this paper is the transportation infrastructure. Z o n e i t * l n t r a i t represents the interaction term of the moderating variable and the disposition variable. The variables l n t r a i t and Z o n e i t are added to the high-dimensional control variables X i t , and the rest of the variables in Eq. 5 are identical to Eq. 1 . θ 1 represents the disposal factor to focus on.

3.2 Variable selection

3.2.1 dependent variable: level of urban green innovation (lngi).

Nowadays, many academics use indicators like the number of applications for patents or authorizations to assess the degree of urban innovation. To be more precise, the quantity of patent applications is a measure of technological innovation effort, while the number of patents authorized undergoes strict auditing and can provide a more direct reflection of the achievements and capacity of scientific and technological innovation. Thus, this paper refers to the studies of Zhou and Shen (2020) and Li X. et al. (2022) to utilize the count of authorized green invention patents in each prefecture-level city to indicate the level of green innovation. For the empirical study, the count of authorized green patents plus 1 is transformed using logarithm.

3.2.2 Disposal variable: dummy variables for national high-tech zones (Zone)

The national high-tech zone dummy variable’s value correlates with the city in which it is located and the list of national high-tech zones released by China’s Ministry of Science and Technology. If a national high-tech zone was established in the city by 2017, the value is set to 1 for the year the high-tech zone is established and subsequent years. Otherwise, it is set to 0.

3.2.3 Moderating variable: transportation infrastructure (lntra)

Previous studies have shown that China’s highway freight transport comprises 75% of the total freight transport ( Li and Tang, 2015 ). Highway transportation infrastructure has a significant influence on the evolution of the Chinese economy. The development and improvement of highway infrastructure are crucial for modern transportation. This paper uses the research methods of Wu (2019) and uses the roadway mileage (measured in kilometers) to population as a measure of the quality of the transportation system.

3.2.4 Control variables

(1) Foreign direct investment (lnfdi): There is general agreement among academics that foreign direct investment (FDI) significantly influences urban green innovation, as FDI provides expertise in management, human resources, and cutting-edge industrial technology ( Luo et al., 2021 ). Thus, it is necessary to consider and control the level of FDI. This paper uses the ratio of foreign investment to the local GDP in a million yuan.

(2) Financial development level (lnfd): Innovation in science and technology is greatly aided by finance. For the green innovation-driven strategy to advance, it is imperative that funding for science and technology innovation be strengthened. The amount of capital raised for innovation is strongly impacted by the state of urban financial development ( Zhou and Du, 2021 ). Thus, this paper uses the loan balance to GDP ratio as an indicator.

(3) Human capital (lnhum): Highly skilled human capital is essential for cities to drive green innovation. Generally, highly qualified human capital significantly boosts green innovation ( Ansaris et al., 2016 ). Therefore, a measure was employed: the proportion of people in the city who had completed their bachelor’s degree or above.

(4) Industrial structure (lnind): Generally, the secondary industry in China is the primary source of pollution, and there is a significant impact of industrial structure on green innovation ( Qiu et al., 2023 ). The metric used in this paper is the secondary industry-to-GDP ratio for the area.

(5) Regional economic development level (lnagdp): A region’s level of economic growth is indicative of the material foundation for urban green innovation and in-fluences the growth of green innovation in the region ( Bo et al., 2020 ). This research uses the annual gross domestic product per capita as a measurement.

3.3 Data source

By 2017, China had developed 157 national high-tech zones in total. In conjunction with the study’s objectives, this study performs sample adjustments and a screening process. The study’s sample period spans from 2007 to 2019. 57 national high-tech zones that were created prior to 2000 are omitted to lessen the impact on the test results of towns having high-tech zones founded before 2007. Due to the limitations of high-tech areas in cities at the county level in promoting urban green innovation, 8 high-tech zones located in county-level cities are excluded. And 4 high-tech zones with missing severe data are excluded. Among the list of established national high-tech zones, 88 high-tech zones are distributed across 83 prefecture-level cities due to multiple districts within a single city. As a result, 83 cities are selected as the experimental group for this study. Additionally, a control group of 80 cities was selected from among those that did not have high-tech zones by the end of 2019, resulting in a final sample size of 163 cities. This paper collects green patent data for each city from the China Green Patent Statistical Report published by the State Intellectual Property Office. The author compiled the list of national high-tech zones and the starting year of their establishment on the official government website. In addition, the remaining data in this paper primarily originated from the China Urban Statistical Yearbook (2007–2019), the EPS database, and the official websites of the respective city’s Bureau of Statistics. Missing values were addressed through linear interpolation. To address heteroskedasticity in the model, the study logarithmically transforms the variables, excluding the disposal variable. Table 1 shows the descriptive analysis of the variables.

Table 1 . Descriptive analysis.

4 Empirical analysis

4.1 national high-tech zones’ policy effects on urban green innovation.

This study utilizes the DML model to estimate the impact of industrial policies implemented in national high-tech zones at the level of urban green innovation. Following the approach of Zhang and Li (2023) , the sample is split in a ratio of 1:4, and the random forest algorithm is used to perform predictions and combine Eq. ( 1 ) with Eq. ( 4 ) for the regression. Table 2 presents the results with and without controlling for time and city effects. The results indicate that the treatment effect sizes for these four columns are 0.376, 0.293, 0.396, and 0.268, correspondingly, each of which was significant at a 1% level. Thus, Hypothesis 1 is supported.

Table 2 . Benchmark regression results.

4.2 Robustness tests

4.2.1 eliminate the influence of extreme values.

To reduce the impact of extreme values on the estimation outcomes, all variables on the benchmark regression, excluding the disposal variable, undergo a shrinkage process based on the upper and lower 1% and 5% quantiles. Values lower than the lowest and higher than the highest quantile are replaced accordingly. Regression analyses are conducted. Table 3 demonstrates that removing outliers did not substantially alter the findings of this study.

Table 3 . Extreme values removal results.

4.2.2 Considering province-time interaction fixed effects

Since provinces are critical administrative units in the governance system of the Chinese government, cities within the same province often share similarities in policy environment and location characteristics. Therefore, to account for the influence of temporal changes across different provinces, this study incorporates province-time interaction fixed effects based on the benchmark regression. Table 4 presents the individual regression results. Based on the regression results, after accounting for the correlation between different city characteristics within the same province, national high-tech zone policies continue to significantly influence urban green innovation, even at the 1% level.

Table 4 . The addition of province and time fixed effects interaction terms.

4.2.3 Excluding other policy disturbances

When analyzing how national high-tech zones affect strategy for urban green innovation, it is susceptible to the influence of concurrent policies. This study accounts for other comparable policies during the same period to ensure an accurate estimation of the policy effect. Since 2007, national high-tech zone policies have been successively implemented, including the development of “smart cities.” Therefore, this study incorporates a policy dummy variable for “smart cities” in the benchmark regression. The specific regression findings are shown in Table 5 . After controlling for the impact of concurrent policies, the importance of national high-tech zones’ policy impact remains consistent.

Table 5 . Results of removing the impact of parallel policies.

4.2.4 Resetting the DML model

To mitigate the potential bias introduced by the settings in the DML model on the conclusions, the purpose of this study is to assess the conclusions’ robustness using the following methods. First, the sample split ratio of the DML model is adjusted from 1:4 to 1:2 to examine the potential impact of the sample split ratio on the conclusions of this study. Second, the machine learning algorithm is substituted, replacing the random forest algorithm, which has been utilized as a prediction algorithm, with lasso regression, gradient boosting, and neural networks to investigate the potential influence of prediction algorithms on the conclusions of this study. Third, regarding benchmark regression, additional linear models were constructed and analyzed using DML, which involves subjective decisions regarding model form selection. Therefore, DML was employed to construct more comprehensive interactive models, aiming to assess the influence of model settings on the conclusions of this study. The main and auxiliary regressions utilized for the analysis were modified as follows:

Combining Eqs ( 7 ), ( 8 ) for the regression, the interactive model yielded estimated coefficients for the disposition effect:

The results of Eq. ( 9 ) are shown in column (5) of Table 6 . And all the regression results obtained from the modified DML model are presented in Table 6 .

Table 6 . Results of resetting the DML model.

The findings indicate that the sample split ratio in the DML model, the prediction algorithm used, or the model estimation approach does not impact the conclusion that the national high-tech zone policy raises urban areas’ level of green innovation. These factors only modify the magnitude of the policy effect to some degree.

4.3 Heterogeneity analysis

4.3.1 regional heterogeneity.

The sample cities were further divided into the east, central, and west regions based on the three major economic subregions to examine regional variations in national high-tech zone policies ' effects on urban green innovation, with the results presented in Table 7 . National high-tech zone policies do not statistically significantly affect urban green innovation in the eastern region. However, they have a considerable beneficial influence in the central and western areas. The lack of statistical significance may be explained by the possibility that the setting up of national high-tech zones in the eastern region will provide obstacles to the growth of urban green innovation, such as resource strain and environmental pollution. Given the central and western regions’ relatively underdeveloped economic status and industrial structure, coupled with the preceding theoretical analysis, establishing national high-tech zones is a crucial catalyst, significantly boosting urban green innovation levels. Furthermore, the central government emphasizes that setting high-tech national zones should consider regional resource endowments and local conditions, implementing tailored policies. The central and western regions possess unique geographic locations and natural conditions that make them well-suited for developing solar energy, wind energy, and other forms of green energy. Compared to the central region, the national high-tech zone initiative has a more pronounced impact on promoting urban green innovation in the western region. While further optimization is needed for the western region’s urban innovation environment, the policy on national high-tech zones has a more substantial incentive effect in this region due to its more significant development potential, positive transformation of industrial structure, and increased policy support from the state, including the development strategy for the western region.

Table 7 . Heterogeneity test results for different regions.

4.3.2 Urban hierarchical heterogeneity

The New Tier 1 Cities Institute’s ‘2020 City Business Charm Ranking’ is the basis for this study, with the sample cities categorized into Tier 1 (New Tier 1), Tier 2, Tier 3, Tier 4, and Tier 5. Table 8 presents the regression findings for each of the groups.

Table 8 . Heterogeneity test results for different classes of cities.

The results in Table 8 reveal significant heterogeneity at the city level regarding national high-tech zones’ effects on urban green innovation, confirming Hypothesis 2 . In particular, the coefficients for the first-tier cities are not statistically significant due to the small sample size, and the same applies to the fifth-tier cities. This could be attributed to the relatively weak economy and infrastructure development issues in the fifth-tier cities. Additionally, due to their limited level of development, the fifth-tier cities may have a relatively homogeneous industrial structure, with a dominance of traditional industries or agriculture and a need for a more diversified industrial layout. National high-tech zones have not greatly aided the development of green innovation in these cities. In contrast, national high-tech zone policies in second-tier, third-tier, and fourth-tier cities have a noteworthy favorable impact on green innovation, indicating their favorable influence on enhancing green innovation in these cities. Despite the lower level of economic development in fourth-tier cities compared to second-tier and third-tier cities, the fourth-tier cities’ national high-tech zones have the most pronounced impact on promoting green innovation. This could be attributed to the ongoing transformation of industries in fourth-tier cities, which are still in the technology diffusion and imitation stage, allowing these cities’ national high-tech zones to maintain a high marginal effect. Thus, Hypothesis 2 is supported.

5 Further analysis

According to the empirical findings, setting high-tech national zones significantly raises the bar for urban green innovation. Therefore, it is essential to understand the underlying factors and mechanisms that contribute to the positive correlation. This paper constructs a moderating effect test model using Eqs 5 , 6 and provides a detailed discussion by introducing transportation infrastructure as a moderating variable.

The empirical finding of the moderating impact of transportation infrastructure is shown in Table 9 . The dichotomous interaction term Zone*lntra is significantly negative at the 5% level, suggesting that the impact of national high-tech zone policies on the level of urban green innovation is negatively moderated by transportation infrastructure. This result deviates from the general expectation, but it aligns with the complexity of the role played by transportation infrastructure in the context of modern economic development, as discussed in the previous theoretical analysis. This could be attributed to the insufficient green innovation benefits generated by the policy on national high-tech zones at the current stage, which fails to compensate for the adverse effects of excessive resource consumption and environmental pollution caused by the construction of the zone. Furthermore, transportation infrastructure can lead to an excessive concentration of similar enterprises in the high-tech zones. This excessive concentration creates a relative crowding effect, intensifying competition among enterprises. It diminishes their inclination to engage in green innovation collaboration and investment and hinders their effective implementation of technological research and development activities. Moreover, the excessive clustering of similar enterprises implies a need for more diversity in green innovation activities among businesses located in national high-tech zones. This results in duplicated green innovation outputs and hinders the advancement of green innovation. Thus, Hypothesis 3 is supported.

Table 9 . Empirical results of moderating effects.

6 Conclusion and policy recommendations

6.1 conclusion.

Based on panel data from 163 prefecture-level cities in China from 2007 to 2019, the net effect of setting national high-tech zones on urban green innovation was analyzed using the double machine learning model. The results found that: firstly, the national high-tech zone policy significantly raises the degree of local green innovation, and these results remain robust even after accounting for various factors that could affect the estimation results. Secondly, in the central and western regions, the level of urban green innovation is positively impacted by the national high-tech zone policy; However, this impact is less significant in the eastern region. In the western region compared to the central region, the national high-tech zone initiative has a stronger impact on increasing the level of urban green innovation. Across different city levels, compared to second-tier and third-tier cities, the high-tech zone policy has a more substantial impact on increasing the level of green innovation in fourth-tier cities. Thirdly, based on the moderating effect mechanism test, the construction of transportation infrastructure weakens the promotional effect of national high-tech zones on urban green innovation.

6.2 Policy recommendations

In order that national high-tech zones can better promote China’s high-quality development, this paper proposes the following policy recommendations:

(1) Urban green innovation in China depends on accelerating the setting up of national high-tech zones and creating an atmosphere that supports innovation. Establishing national high-tech zones as testbeds for high-quality development and green innovation has significantly elevated urban green innovation. Thus, cities can efficiently foster urban green innovation by supporting the development of national high-tech zones. Cities that have already established national high-tech zones should further encourage enterprises within these zones to increase their investment in research and development. They should also proceed to foster the leadership of national high-tech zones for urban green innovation, assuming the role of pilot cities as models and leaders. Additionally, it is essential to establish mechanisms for cooperation and synergy between the pilot cities and their neighboring cities to promote collective green development in the region.

(2) Expanding the pilot program and implementing tailored policies based on local conditions are essential. Industrial policies about national high-tech zones have differing effects on urban green innovation. Regions should leverage their comparative advantages, consider urban development’s commonalities and unique aspects, and foster a stable and sustainable green innovation ecosystem. The western and central regions should prioritize constructing and enhancing new infrastructure and bolster support for the high-tech green industry. The western region should seize the opportunity presented by national policies that prioritize support, quicken the rate of environmental innovation, and progressively bridge the gap with the eastern and central regions in various aspects. Furthermore, second-tier, third-tier, and fourth-tier cities should enhance the advantages of national high-tech zone policies, further maintaining the high standard of green innovation and keeping green innovation at an elevated level. Regions facing challenges in green innovation, particularly fifth-tier cities, should learn from the development experiences of advanced regions with national high-tech zones to compensate for their deficiencies in green innovation.

(3) Highlighting the importance of transportation regulation and enhancing collaboration in green innovation is crucial. Firstly, transportation infrastructure should be maximized to strengthen coordination and cooperation among regions, facilitate the smooth movement of innovative talents across regions, and facilitate the rational sharing of innovative resources, collectively enhancing green innovation. Additionally, attention ought to be given to the industrial clustering effect of parks to prevent the wastage of resources and inefficiencies resulting from the excessive clustering of similar industries. Efforts should be focused on effectively harnessing the latent potential of crucial transportation infrastructure areas as long-term drivers of development, promptly mitigating the negative impact of transportation infrastructure construction, and gradually achieving the synergistic promotion of the setting up of national high-tech zones and the raising of urban levels of green innovation, among other overarching objectives.

6.3 Limitations and future research

Our study has some limitations because the research in this paper is conducted in the institutional context of China. For example, not all countries are suitable for implementing similar industrial policies to develop the economy while focusing on environmental protection. However, we recognize that this study is interesting and relevant, and it encourages us to focus more intensely on environmental protection from an industrial policy perspective. Moreover, this paper exhibits certain limitations in the research process. Firstly, the urban green innovation measurement index was developed using the quantity of green patent authorizations. Future studies could focus on green innovation processes, such as the quality of green patents granted. Secondly, the paper employs machine learning techniques for causal inference. Subsequent investigations could delve further into the potential applications of machine learning algorithms in environmental sciences to maximize the benefits of innovative research methodologies.

Data availability statement

The raw data supporting the conclusion of this article will be made available by the authors, without undue reservation.

Author contributions

WC: Conceptualization, Data curation, Formal Analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Supervision, Validation, Visualization, Writing–review and editing. YJ: Conceptualization, Data curation, Formal Analysis, Investigation, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing–original draft, Writing–review and editing. BT: Investigation, Project administration, Writing–review and editing.

The authors declare that financial support was received for the research, authorship, and/or publication of this article. This research was supported by the Youth Fund for Humanities and Social Science research of Ministry of Education (20YJC790004).

Acknowledgments

The authors are grateful to the editors and the reviewers for their insightful comments.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher’s note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Albahari, A., Pérez-Canto, S., Barge-Gil, A., and Modrego, A. (2017). Technology parks versus science parks: does the university make the difference? Technol. Forecast. Soc. Change 116, 13–28. doi:10.1016/j.techfore.2016.11.012

CrossRef Full Text | Google Scholar

Alder, S., Shao, L., and Zilibotti, F. (2016). Economic reforms and industrial policy in a panel of Chinese cities. J. Econ. Growth 21, 305–349. doi:10.1007/s10887-016-9131-x

Ansaris, M., Ashrafi, S., and Jebellie, H. (2016). The impact of human capital on green innovation. Industrial Manag. J. 8 (2), 141–162. doi:10.22059/imj.2016.60653

Banister, D., and Berechman, Y. (2001). Transport investment and the promotion of economic growth. J. Transp. Geogr. 9 (3), 209–218. doi:10.1016/s0966-6923(01)00013-8

Behrens, K., Lamorgese, A. R., Ottaviano, G. I., and Tabuchi, T. (2007). Changes in transport and non-transport costs: local vs global impacts in a spatial network. Regional Sci. Urban Econ. 37 (6), 625–648. doi:10.1016/j.regsciurbeco.2007.08.003

Bo, W., Yongzhong, Z., Lingshan, C., and Xing, Y. (2020). Urban green innovation level and decomposition of its determinants in China. Sci. Res. Manag. 41 (8), 123. doi:10.19571/j.cnki.1000-2995.2020.08.013

Cao, Q. F. (2019). The latest researches on place based policy and its implications for the construction of xiong’an national new district. Sci. Technol. Prog. Policy 36 (2), 36–43. (in Chinese).

Google Scholar

Cattapan, P., Passarelli, M., and Petrone, M. (2012). Brokerage and SME innovation: an analysis of the technology transfer service at area science park, Italy. Industry High. Educ. 26 (5), 381–391. doi:10.5367/ihe.2012.0119

Chandrashekar, D., and Bala Subrahmanya, M. H. (2017). Absorptive capacity as a determinant of innovation in SMEs: a study of Bengaluru high-tech manufacturing cluster. Small Enterp. Res. 24 (3), 290–315. doi:10.1080/13215906.2017.1396491

Chen, M., and Zheng, Y. (2008). China's regional disparity and its policy responses. China & World Econ. 16 (4), 16–32. doi:10.1111/j.1749-124x.2008.00119.x

Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., et al. (2018). Double/debiased machine learning for treatment and structural parameters. Econ. J. 21 (1), C1–C68. doi:10.1111/ectj.12097

De Beule, F., and Van Beveren, I. (2012). Does firm agglomeration drive product innovation and renewal? An application for Belgium. Tijdschr. Econ. Soc. Geogr. 103 (4), 457–472. doi:10.1111/j.1467-9663.2012.00715.x

Díez-Vial, I., and Fernández-Olmos, M. (2017). The effect of science and technology parks on firms’ performance: how can firms benefit most under economic downturns? Technol. Analysis Strategic Manag. 29 (10), 1153–1166. doi:10.1080/09537325.2016.1274390

Fang, Z., Kong, X., Sensoy, A., Cui, X., and Cheng, F. (2021). Government’s awareness of environmental protection and corporate green innovation: a natural experiment from the new environmental protection law in China. Econ. Analysis Policy 70, 294–312. doi:10.1016/j.eap.2021.03.003

Fritsch, M., and Slavtchev, V. (2011). Determinants of the efficiency of regional innovation systems. Reg. Stud. 45 (7), 905–918. doi:10.1080/00343400802251494

He, J. A., Peng, F. P., and Xie, X. Y. (2022). Mixed-ownership reform, political connection and enterprise innovation: based on the double/unbiased machine learning method. Sci. Technol. Manag. Res. 42 (11), 116–126. (in Chinese).

Hong, J., Feng, B., Wu, Y., and Wang, L. (2016). Do government grants promote innovation efficiency in China's high-tech industries? Technovation 57, 4–13. doi:10.1016/j.technovation.2016.06.001

Huang, W. J., and Fernández-Maldonado, A. M. (2016). High-tech development and spatial planning: comparing The Netherlands and Taiwan from an institutional perspective. Eur. Plan. Stud. 24 (9), 1662–1683. doi:10.1080/09654313.2016.1187717

Hull, I., and Grodecka-Messi, A. (2022). Measuring the impact of taxes and public services on property values: a double machine learning approach . arXiv preprint arXiv:2203.14751.

Jiang, T. (2022). Mediating effects and moderating effects in causal inference. China Ind. Econ. 5, 100–120. doi:10.19581/j.cnki.ciejournal.2022.05.005

Knittel, C. R., and Stolper, S. (2021). Machine learning about treatment effect heterogeneity: the case of household energy use. Nashv. TN 37203, 440–444. doi:10.1257/pandp.20211090

Li, H., and Tang, L. (2015). Transportation infrastructure investment, spatial spillover effect and enterprise inventory. Manag. World 4, 126–136. doi:10.19744/j.cnki.11-1235/f.2015.04.012

Li, W. H., Liu, F., and Liu, T. S. (2022a). Can national high-tech zones improve the urban innovation efficiency? an empirical test based on the effect of spatial agglomeration regulation. Manag. Rev. 34 (5), 93. doi:10.14120/j.cnki.cn11-5057/f.2022.05.007

Li, X. (2015). Analysis and outlook of the related researches on green innovation. R&D Manag. 27 (2), 1–11. doi:10.13581/j.cnki.rdm.2015.02.001

Li, X., Shao, X., Chang, T., and Albu, L. L. (2022b). Does digital finance promote the green innovation of China's listed companies? Energy Econ. 114, 106254. doi:10.1016/j.eneco.2022.106254

Li, X. P., Li, P., Lu, D. G., and Jiang, F. T. (2015). Economic agglomeration, selection effects and firm productivity. J. Manag. World 4, 25–37+51. (in Chinese). doi:10.19744/j.cnki.11-1235/f.2015.04.004

Liu, R. M., and Zhao, R. J. (2015). Does the national high-tech zone promote regional economic development? A verification based on differences-in-differences method. J. Manag. World 8, 30–38. doi:10.19744/j.cnki.11-1235/f.2015.08.005

Luo, R., and Wang, Q. M. (2023). Does the construction of national demonstration logistics park produce economic growth effect? Econ. Surv. 40 (1), 47–56. doi:10.15931/j.cnki.1006-1096.2023.01.015

Luo, Y., Salman, M., and Lu, Z. (2021). Heterogeneous impacts of environmental regulations and foreign direct investment on green innovation across different regions in China. Sci. total Environ. 759, 143744. doi:10.1016/j.scitotenv.2020.143744

PubMed Abstract | CrossRef Full Text | Google Scholar

Melitz, M. J., and Ottaviano, G. I. (2008). Market size, trade, and productivity. Rev. Econ. Stud. 75 (1), 295–316. doi:10.1111/j.1467-937x.2007.00463.x

Park, S. C., and Lee, S. K. (2004). The regional innovation system in Sweden: a study of regional clusters for the development of high technology. Ai Soc. 18 (3), 276–292. doi:10.1007/s00146-003-0277-7

Pokharel, R., Bertolini, L., Te Brömmelstroet, M., and Acharya, S. R. (2021). Spatio-temporal evolution of cities and regional economic development in Nepal: does transport infrastructure matter? J. Transp. Geogr. 90, 102904. doi:10.1016/j.jtrangeo.2020.102904

Qiu, Y., Wang, H., and Wu, J. (2023). Impact of industrial structure upgrading on green innovation: evidence from Chinese cities. Environ. Sci. Pollut. Res. 30 (2), 3887–3900. doi:10.1007/s11356-022-22162-1

Sosnovskikh, S. (2017). Industrial clusters in Russia: the development of special economic zones and industrial parks. Russ. J. Econ. 3 (2), 174–199. doi:10.1016/j.ruje.2017.06.004

Sun, Z., and Sun, J. C. (2015). The effect of Chinese industrial policy: industrial upgrading or short-term economic growth. China Ind. Econ. 7, 52–67. (in Chinese). doi:10.19581/j.cnki.ciejournal.2015.07.004

Tan, J., and Zhang, J. (2018). Does national high-tech development zones promote the growth of urban total factor productivity? —based on" quasi-natural experiments" of 277 cities. Res. Econ. Manag. 39 (9), 75–90. doi:10.13502/j.cnki.issn1000-7636.2018.09.007

Vásquez-Urriago, Á. R., Barge-Gil, A., Rico, A. M., and Paraskevopoulou, E. (2014). The impact of science and technology parks on firms’ product innovation: empirical evidence from Spain. J. Evol. Econ. 24, 835–873. doi:10.1007/s00191-013-0337-1

Wang, F., Dong, M., Ren, J., Luo, S., Zhao, H., and Liu, J. (2022a). The impact of urban spatial structure on air pollution: empirical evidence from China. Environ. Dev. Sustain. 24, 5531–5550. doi:10.1007/s10668-021-01670-z

Wang, M., and Liu, X. (2023). The impact of the establishment of national high-tech zones on total factor productivity of Chinese enterprises. China Econ. 18 (3), 68–93. doi:10.19602/j.chinaeconomist.2023.05.04

Wang, Q., She, S., and Zeng, J. (2020). The mechanism and effect identification of the impact of National High-tech Zones on urban green innovation: based on a DID test. China Popul. Resour. Environ. 30 (02), 129–137.

Wang, W. S., and Xu, T. S. (2020). A research on the impact of national high-teach zone establishment on enterprise innovation performance. Econ. Surv. 37 (6), 76–87. doi:10.15931/j.cnki.1006-1096.20201010.001

Wang, Z., Yang, Y., and Wei, Y. (2022b). Has the construction of national high-tech zones promoted regional economic growth? empirical research from prefecture-level cities in China. Sustainability 14 (10), 6349. doi:10.3390/su14106349

Weber, M., Driessen, P. P., and Runhaar, H. A. (2014). Evaluating environmental policy instruments mixes; a methodology illustrated by noise policy in The Netherlands. J. Environ. Plan. Manag. 57 (9), 1381–1397. doi:10.1080/09640568.2013.808609

Wu, Y. B. (2019). Does fiscal decentralization promote technological innovation. Mod. Econ. Sci. 41, 13–25.

Xu, S. D., Jiang, J., and Zheng, J. (2022). Has the establishment of national high-tech zones promoted industrial Co-Agglomeration? an empirical test based on difference in difference method. Inq. into Econ. Issues 11, 113–127. (in Chinese).

Yang, F., and Guo, G. (2020). Fuzzy comprehensive evaluation of innovation capability of Chinese national high-tech zone based on entropy weight—taking the northern coastal comprehensive economic zone as an example. J. Intelligent Fuzzy Syst. 38 (6), 7857–7864. doi:10.3233/jifs-179855

Yu, N., De Jong, M., Storm, S., and Mi, J. (2013). Spatial spillover effects of transport infrastructure: evidence from Chinese regions. J. Transp. Geogr. 28, 56–66. doi:10.1016/j.jtrangeo.2012.10.009

Yuan, H., and Zhu, C. L. (2018). Do national high-tech zones promote the transformation and upgrading of China’s industrial structure. China Ind. Econ. 8, 60–77. doi:10.19581/j.cnki.ciejournal.2018.08.004

Zhang, T., Chen, L., and Dong, Z. (2018). Highway construction, firm dynamics and regional economic efficiency. China Ind. Econ. 1, 79–99. doi:10.19581/j.cnki.ciejournal.20180115.003

Zhang, T., and Li, J. C. (2023). Network infrastructure, inclusive green growth, and regional inequality: from causal inference based on double machine learning. J. Quantitative Technol. Econ. 40 (4), 113–135. doi:10.13653/j.cnki.jqte.20230310.005

Zhou, L., and Shen, K. (2020). National city group construction and green innovation. China Popul. Resour. Environ. 30 (8), 92–99.

Zhou, X., and Du, J. (2021). Does environmental regulation induce improved financial development for green technological innovation in China? J. Environ. Manag. 300, 113685. doi:10.1016/j.jenvman.2021.113685

Keywords: national high-tech zone, industrial policy, green innovation, heterogeneity analysis, moderating effect, double machine learning

Citation: Cao W, Jia Y and Tan B (2024) Impact of industrial policy on urban green innovation: empirical evidence of China’s national high-tech zones based on double machine learning. Front. Environ. Sci. 12:1369433. doi: 10.3389/fenvs.2024.1369433

Received: 12 January 2024; Accepted: 15 March 2024; Published: 04 April 2024.

Reviewed by:

Copyright © 2024 Cao, Jia and Tan. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Yu Jia, [email protected]