Time-varying impacts of green credit on carbon productivity in China: New evidence from a non-parametric panel data model.

In the context of global climate change threatening human survival, and in a post-pandemic era that advocates for a global green and low-carbon economic recovery, conducting an in-depth analysis to assess whether green finance can effectively support low-carbon economic development from a dynamic perspective is crucial. Unlike existing research, which focuses solely on the average effects of green credit (GC) on carbon productivity (CP), we introduce a non-parametric panel data model to investigate GC's impact on CP across 30 provinces in China from 2003 to 2021, verifying a significant time-varying effect. Specifically, during the first phase (2003-2008), GC negatively impacted CP. In the second phase (2009-2014), this negative influence gradually diminished and transformed into a positive effect. In the third phase (2015-2021), GC continued to positively influence CP, although this effect became insignificant during the pandemic. Further subgroup analysis reveals that in the regions with low environmental regulations, GC did not significantly boost CP throughout the sample period. In contrast, in the regions with high environmental regulations, GC's positive effect persisted in the mid to late stages of the sample period. Additionally, compared to the regions with low levels of marketization, the impact of GC on CP was more pronounced in highly marketized regions. This indicates that the promoting effect of GC on CP depends on strong support from environmental regulations and well-functioning market mechanisms. By adopting a non-parametric approach, this study reveals variations in the impact of GC on CP across different stages and under the influence of the pandemic shock, offering new insights into the relationship between GC and China's CP.

Going green while getting lean: Decomposing carbon and green total factor productivity.

While research on carbon productivity is growing rapidly, the role of structural change in green transition remains unexplored due to the scarcity of firm-level emission data. This study addresses this gap by utilizing unique register-based greenhouse gas emission data from Finland's energy-intensive manufacturing firms for 2000-2019. Applying a structural change productivity decomposition, we break down the sector's carbon productivity and green total factor productivity into four components: contributions from non-switching continuing firms, industry-switching continuing firms, the effects of entry and exit, and resource allocation. The findings highlight the important role of structural change in the sector's productivity. Non-switching continuing firms emerged as the key drivers of both carbon and green total factor productivity growth. The contribution of entry and exit was negative during the financial crisis, while inefficient resource allocation significantly hindered productivity growth throughout the study period. These findings emphasize the importance of public subsidies targeted at environmentally efficient firms to enhance their competitiveness under challenging market conditions. Furthermore, the establishment of a stable yet positive carbon price would incentivize less-productive firms to adopt environmentally friendly technologies.

How does environmental regulation affect carbon productivity? The role of green technology progress and pollution transfer.

This empirical study examines the impact of environmental regulations on carbon productivity under varying conditions using panel data from Chinese provinces from 2011 to 2019. Prior research has reported inconsistent results regarding the relationships between these variables. We developed a spatial Durbin model (SDM) and tested the non-linear effects of environmental regulation on carbon productivity from a spatial linkage perspective. The results demonstrate a U-shaped curve representing the local-neighborhood effect of environmental regulations on carbon productivity. This curve is further dissected into two components: the average direct effect (ADE) and the average indirect effect (AIE). Furthermore, the findings indicate that green technology progress and pollution transfer act as moderating factors in shaping the U-shaped curve. Green technological progress has steepened the U-shape curve, whereas pollution transfer has flattened it. Based on these findings, we propose three recommendations for the formulation of environmental regulation policies.

Environmental regulation and the growth of the total-factor carbon productivity of China's industries: Evidence from the implementation of action plan of air pollution prevention and control.

To abate the severe air pollution, the Chinese government released the Action Plan of Air Pollution Prevention and Control (APAPPC) in 2013. This paper regards the APAPPC as a quasi-experiment and uses the DID method to investigate the impact of environmental regulation on the growth of green total-factor productivity of China's industries. This article employs the non-radial directional distance function (NDDF) and the global Malmquist index to measure the total-factor carbon productivity of China's industries. Further regressions suggest that the implementation of the APAPPC has significantly promoted the growth of the total-factor carbon productivity in the air pollution-intensive industries, and its marginal effect has steadily increased with time. This result is still valid after using a series of counterfactual tests and robustness tests. The further mechanism study shows that the APAPPC has significantly promoted R&D investment, especially in instruments and equipment, which has effectively promoted technical efficiency and technological advancement. It indicates that stringent and well-designed environmental regulations should lead to a "win-win" situation of environmental improvement and economic development by encouraging enterprises to upgrade their technology and equipment.

Impact of digitization on carbon productivity: an empirical analysis of 136 countries.

Enhancing carbon productivity (CP) is key to achieving carbon reduction goals while maintaining economic growth. Digital technology plays a significant role in improving CP. Based on panel data from 136 countries worldwide from 2000 to 2020, this study empirically examines the impact of digitalization on CP and its mechanisms using fixed-effects and mediation models. The conclusions are as follows: (1) Overall, digitalization significantly enhances CP. (2) In terms of the mechanism, digitalization primarily improves CP through technological innovation and mitigating income inequality. (3) In terms of the quantile regression results, as the quantile level of CP increases, the promoting effect of digitalization on CP gradually strengthens. (4) From the perspective of heterogeneity among regions, income levels and human capital levels, digitalization has the greatest promotion effect on carbon productivity in European countries, high-income countries and high human capital countries. This study provides a reference for policymakers worldwide to use digital technology in achieving carbon emission reduction targets.

Repercussions of environmental policy stringency on carbon, energy and non-energy productivity in highly emerging economies: perspective of green growth.

Emerging economies have prioritized the enhancement of carbon and energy productivity to uphold environmental integrity. Consequently, the policymakers introduced the environmental policy stringency measure to control emission activities. Accordingly, this study explores the environmental stringency policy's impact on carbon, energy, and non-energy productivity over the period of 1995-2020. This study addresses the impact of environmental policy stringency on quality of life (exposure to environmental risk). Regardless of variation, growing economies have higher carbon productivity. It is worth noting that energy productivity compared to carbon is higher. Based on the findings derived from the CS-ARDL model, it can be concluded that environmental stringency policies significantly positively impact carbon productivity in emerging countries. Economies that implement stringent environmental rules have the potential to enhance both energy and non-energy productivity to a greater extent. Meanwhile, the environmental policy effectively reduces environmental risk exposure and increases the quality of life. Environmental technology is inefficient in promoting emerging economies' environmental productivity. Similarly, trade promotes carbon activities and may involve comparative advantage race, pollution heaven hypothesis possible to exist. This study provides empirical evidence supporting the notion that investing in human capital is crucial in enhancing productivity. The findings suggest a more comprehensive and integrated approach to environmental policy in rising economies. This all-encompassing strategy is considered crucial for making significant gains in carbon productivity and simultaneously promoting sustainable green growth.

Spatial spillover effects of manufacturing agglomeration on China's total factor carbon productivity: evidence from the regulatory role of fiscal decentralization.

Based on the theory of new economic geography, this paper used panel data from 284 cities in China from 2006 to 2019 to determine the effects of spatial spillover and heterogeneity of manufacturing agglomeration (MA) on urban total factor carbon productivity (TFCP) and the regulatory effect of fiscal decentralization on the outcome. The results showed that (1) MA shifted from the eastern region of China to the central and western regions, with the center of gravity moving towards the south. The spatial pattern of urban TFCP displayed the solidifying characteristics of "low-level agglomeration and high-level dispersion," with technological progress being the primary driver. (2) The MA had a significant U-shaped effect on the TFCP of local and nearby cities, promoting the improvement of urban TFCP through Jacobs' and Porter's externalities but not MAR externalities. (3) Regarding regional differences, MA had a significant U-shaped impact on urban TFCP in the eastern and central regions. At different levels of manufacturing development, there was an inverted U-shaped relationship in both manufacturing and non-manufacturing metropolitan areas. For varying degrees of manufacturing industry agglomeration, increasing the level of MA improved urban TFCP in low agglomeration areas, but inhibited it in high agglomeration areas. (4) The implementation of fiscal decentralization nationwide and in the eastern and central regions significantly weakened the impact of MA on TFCP. Furthermore, fiscal decentralization significantly enhanced the influence of MA on TFCP in cities exhibiting high levels of economic development, advanced industrial structure and strict environmental regulations. Therefore, based on the unique developmental characteristics of the manufacturing industry in various cities, fiscal policies ought to be tailored to local circumstances to support key areas. This strategy should facilitate the high-quality development of manufacturing industry and low-carbon development of cities.

How does the development of the digital economy influence carbon productivity? The moderating effect of environmental regulation.

Improving carbon productivity is of great significance to China's "30 · 60" carbon target, while the development of the digital economy is a driving force for green transformation. However, few studies discuss the relationship between the digital economy and carbon productivity. We investigate the influence of digital economic development on carbon productivity using panel data from 30 Chinese provinces from 2011 to 2020. Spatial econometric and moderating effects are considered. The results show that (i) digital economy has a positive direct and negative spatial spillover effect on carbon productivity, and this conclusion is still valid after the robustness test and endogeneity test; (ii) digital infrastructure has a greater impact on carbon productivity than digital industrialization and industrial digitalization; (iii) the mechanism analysis shows that environmental regulation negatively moderates the relationship between the digital economy and carbon productivity; (iv) heterogeneity analysis shows that the effect of the digital economy on carbon productivity is more obvious in the central region compared to the western region, while it is not significant in the eastern region. Overall, this paper not only provides a new analytical perspective for understanding the improvement of carbon productivity in the digital economy but also provides policy inspiration for promoting carbon peak and carbon neutrality goals.

Does international trade improve carbon productivity? Evidence from countries along the Belt and Road.

The Belt and Road Initiative proposed by China has significantly increased trade in countries along the Belt and Road (B&R). Since most of these countries are developing and emerging economies, the pressure to reduce carbon emissions poses a leading challenge for them. Carbon productivity has become a key indicator for assessing the degree of low-carbon development, as it can link economic development with CO2 emission reduction. However, few studies have investigated how international trade affects carbon productivity. Based on panel data from 43 countries along the B&R during 2001-2019, this paper uses a system GMM model to explore the impact of international trade on carbon productivity. Then, we divide the 43 countries in the sample into two groups according to their income levels to compare the different effects of international trade on carbon productivity. The results show that, first, the carbon productivity of the examined B&R countries has an overall increasing trend, and there is a significant heterogeneity of carbon productivity among countries with different income levels. Second, the effects of international trade, export, and import on carbon productivity are all significantly positive, and export's effect is higher than import. In the high-income group, carbon productivity is more likely to be improved by trade than in the middle (low)-income group. Third, economic development level, urbanization, and energy productivity are positively associated with carbon productivity, while CO2 per capita and government size inhibit carbon productivity improvement. Insight into the impact of international trade on carbon productivity provides theoretical support for B&R countries to better leverage foreign trade activities to achieve a green economy.

Evaluating the carbon total factor productivity of China: based on Cobb-Douglas production function.

This paper introduces a novel methodology for estimating carbon total factor productivity based on the Cobb-Douglas production function. The research method introduced in this article expands upon the framework for assessing total factor productivity through the Solow residual method. It unifies the conceptual and methodological aspects of carbon total factor productivity with those of single factor productivity. Utilizing panel data from various provinces between 2010 and 2021, we computed carbon total factor productivity to understand its implications for China in combating global climate change. We demonstrate that (i) we have introduced a method to handle negative coefficients in the Cobb-Douglas production function by incorporating productivity in lieu of input factors during calculations. (ii) Carbon total factor productivity, encapsulating the geometric weighted mean of labor, capital, and carbon productivity, holds notable economic relevance. Further, it serves as an integrative metric comprising carbon productivity intertwined with the mean labor and capital carbon factors. And (iii) the influence of carbon total factor productivity growth on economic progression remains relatively subdued, with escalating labor force growth posing detrimental effects on several provincial economies. Enhancing carbon total factor productivity emerges as an imperative to harmonize robust economic growth with strategic carbon curtailment. Our analytical framework provides nuanced perspectives on productivity determinants, accentuating the thrust towards sustainable evolution amidst climatic challenges. This investigation bears profound significance for policymakers endeavoring to sculpt a carbon-conscious economic paradigm in consonance with global climatic ambitions.

Spatial effects of trade, foreign direct investment (FDI), and natural resource rents on carbon productivity in the GCC region.

Background: Natural resource rents (NRRs) may determine the environment and economic growth of the GCC countries due to their over-reliance on the natural resource sector. NRRs are the source of income in resource-abundant GCC countries. So, increasing income of these countries could pollute the environment by increasing overall economic activities. Consequently, NRRs could determine carbon productivity in the GCC region through increasing income and carbon emissions. Methods: The effects of trade openness (TO), foreign direct investment (FDI), urbanization, and oil and natural gas rents on carbon productivity (CP) are examined in the GCC region from 1980-2021 using the spatial Durbin model. Results: The CP of the GCC countries has spillovers in their neighboring countries. Oil rent reduces carbon productivity in domestic economies and the entire GCC region. Natural gas rent, TO, and FDI increase, and urbanization reduces carbon productivity in neighboring economies and the entire GCC region. Moreover, urbanization reduces carbon productivity in domestic economies as well. The study recommends the GCC countries to reduce reliance on oil rent and increase globalization in terms of TO and FDI in the region to promote carbon productivity. Moreover, GCC countries should also focus more on natural gas rent instead of oil rent to raise carbon productivity.

Spatiotemporal dynamics and influencing factors of carbon productivity in counties of Shandong Province, China.

In the context of the increasing global greenhouse effect, the Chinese government has proposed a "dual carbon" target. As a major carbon-emitting province in China, Shandong Province needs to improve its carbon productivity to coordinate carbon emission reductions and sustainable economic growth. This study analyzes the spatial and temporal evolution of carbon productivity at the county scale and the factors influencing it in Shandong Province from 2000 to 2017. The study uses the Dagum Gini coefficient, kernel density analysis, spatial autocorrelation model, and geographically and temporally weighted regression model. The results indicate that the carbon productivity in Shandong Province nearly doubled during the study period, revealing a spatial distribution characteristic of "high in the east and low in the west," together with a significant positive spatial autocorrelation. Intra-regional differences, the most important source of development differences among the three economic circles, rose to 32.11% during the study period, whereas inter-regional differences declined to 26.6%. Gross domestic product per capita and population density play a significant positive role in the development of carbon productivity. The balance of deposits in financial institutions at the end of the year has a weak positive effect, and the local average public finance expenditure and secondary industry structure on carbon productivity are negative in general. Shandong Province should identify specific regions with weak carbon productivity levels and understand the key factors to improve carbon productivity to promote the achievement of the "dual carbon" goal.

How does science and technology innovation improve carbon productivity?-Evidence at China's province level.

Science and technological innovation play a crucial role in achieving the goals of carbon peaking and carbon neutrality. They are also vital for improving the scale, structure, and allocation of input and output factors, as well as enhancing the marginal efficiency of production factors. This study investigates the impact of science and technological innovation on carbon productivity using data from 30 provinces (cities, and autonomous regions) in China spanning the period from 2004 to 2020. The findings are as follows: First, the spatial characteristics of carbon productivity in China have gradually evolved into "low in the northern regions and high in the southern regions." The spatial distribution of science and technological innovation in China shows a gradual decrease from the southeast to the northwest. Second, employing the fixed effect model, we find that science and technological innovation has a significant positive effect on carbon productivity, with regional variations in the magnitude of the effect. Furthermore, we identify industrial structure rationalization, industrial structure advancement, and industrial structure ecology as important mechanisms through which science and technological innovation influence carbon productivity. The mediation effects are measured at 3.37%, 8%, and 73.94%, respectively. These research findings provide valuable insights for enhancing technological innovation capabilities, realizing low-carbon development, and addressing the challenges posed by climate change.

Spatial differences, dynamic evolution, and convergence of carbon productivity in China.

China is currently developing a green economy, and improving carbon productivity (CAP) is an important part of this process. The current study applied a minimum distance to strong efficient frontier (MinDS) model to measure China's CAP. The Dagum Gini coefficient and kernel density estimation methods were further used to reveal its spatial differences and dynamic evolution, while the coefficient of variation and spatial convergence models were employed to examine its convergence characteristics. The results showed significant spatial differences in China's CAP, with primarily high and low spatial distribution characteristics in the east and west, respectively. Between-regional differences were the main sources of the overall differences. Moreover, the differences between overall, eastern, central, and western regions of China all exhibited a widening trend. Although none showed σ convergence, all had significant absolute ß spatial convergence and conditional ß spatial convergence characteristics. Collectively, the findings of this study objectively reflect the real level, distribution characteristics, and spatial convergence characteristics of CAP in China as a whole and in each region, while also providing a reference basis for achieving peak carbon neutrality.

Do biased technological advances affect carbon productivity of service sector: Evidence from China.

Increasing carbon productivity is regarded as one of the significant ways to strike a balance between national economic growth and environmental protection. As the proportion of China's service sector in the national economy rises and the severity of environmental pollution increases, the matter of carbon productivity in service sector required to be explored in depth. This paper focuses on the biased technological advances index of China's service sector and its impact on carbon productivity by constructing a two-layer nested CES production function, proposing policy countermeasures to raise service sector's carbon productivity in China, which is of great practical significance in reducing carbon emissions of China's service sector, improve carbon productivity of China's service sector and promote the green transformation and sustainable development of China's service sector. The results are as follows. (1) The average value of the biased technological advances index of services in China is negative, indicating that technological advances of services in China are biased towards non-energy elements. The biased technological advances indexes of China's service sector in the western, middle and eastern regions are also negative, and the index of the eastern region is the smallest, indicating that the technological advances of the service sector in the eastern region are biased towards non-energy elements to the highest extent. (2) There is a negative correlation between the biased technological advances index and carbon productivity of services in China. In the western, middle and eastern regions of China, the bias of technological advances in eastern China has the greatest effect on China's productivity of carbon in service sector. The policy implication is that in order to increase China's services' productivity of carbon, it is essential to reduce the biased technological advances index of China's service sector.

Influence mechanisms and spatial spillover effects of industrial agglomeration on carbon productivity in China's Yellow River Basin.

The ecological protection and high-quality development of the Yellow River Basin have become major national strategies in China. Therefore, reducing carbon emissions in the Yellow River Basin through efficient industrial agglomeration is necessary for achieving the goals of carbon peak by 2030 and carbon neutrality by 2060. The Yellow River Basin is an important base for energy, chemicals, raw materials, and industry in China, making it important to study the effects of different industrial agglomeration types on carbon productivity from the perspective of agglomeration externalities. Therefore, taking 2009-2019 panel data for prefecture-level cities in the Yellow River Basin, this study uses a spatial Durbin model to investigate the spatial spillover effects of industrial agglomeration (i.e., specialized, diversified, and competitive agglomeration) on carbon productivity. Furthermore, the moderating effects of urbanization level and environmental regulation are analyzed. The results reveal, first, the existence of spatial correlation in carbon productivity across different cities in the Yellow River Basin. Second, diversified and competitive agglomeration significantly increase carbon productivity, although competitive agglomeration has beggar-thy-neighbor spillover effects. Meanwhile, the effect of specialized agglomeration is not significant. Third, the effects of different types of industrial agglomeration differ significantly between cities in different locations and with different resource endowments. Fourth, urbanization level and environmental regulation have different moderating effects in the relationship between different types of industrial agglomeration and carbon productivity. These findings provide evidence for further developing rational industrial agglomeration patterns to enhance carbon productivity in the Yellow River Basin.

The threshold effect of environmental regulation in the nexus between green finance and total factor carbon productivity: evidence from a dynamic panel threshold model.

This paper investigates whether the effect of regional green finance (GF) on total factor carbon productivity (TFCP) differs by the level of environmental regulation intensity. Using data of 30 provinces in China from 2008 to 2020, we measure green finance based on the entropy weight method. Then, we employ the Super-slacks-based measure (SBM) model that incorporates undesirable outputs to calculate TFCP. Further, based on a dynamic panel threshold model, we empirically investigate the relationship between GF and TFCP. The main conclusions are as follows: (1) a nonlinear relationship exists between GF and TFCP in China. Moreover, we are the first to take market-based environmental regulation (MER) and command-and-control environmental regulation (CER) as threshold variables. With MER and CER as threshold variables, there was a double-threshold effect between both GF and TFCP. Hence, when GF has a negative impact on TFCP, the government should consider reasonably adjusting environmental regulations to reap the full benefits of GF in terms of TFCP growth. (2) The heterogeneity analysis states that the nonlinear effect of GF on TFCP in different regions is still significant under the thresholds of MER and CER. Therefore, the government should implement flexible environmental regulation policies to make GF promote TFCP according to region's characteristics. This study provides a new perspective on the relationship between green finance and total factor carbon productivity.

Assessing the digital economy and its effect on carbon performance: the case of China.

With China's carbon neutrality target and the rapid growth of the digital economy, it is critical to understand how the digital economy can decouple economic growth from carbon emissions. This paper innovatively calculates the digital economy index in China from 2004 to 2019 and explores how the digital economy affects total factor carbon productivity (TFCP) and its spatial spillover effect. The empirical results indicate that (1) the development level of digital economy in eastern provinces is significantly higher than that in other provinces. (2) The digital economy positively promotes TFCP. Interestingly, digital industrialization has a more substantial effect on improving TFCP, while industry digitization has a weaker effect. (3) The digital economy not only helps improve the local TFCP but also has spatial spillovers to the surrounding areas and has the most prominent effect on the TFCP of the northern. (4) The digital economy affects TFCP through four channels industrial digital upgrading, human capital, market integration, and resource allocation. (5) The effects of the digital economy on TFCP exhibit significant heterogeneity in terms of time, region, and pollution degree. The findings of this study have important policy implications for promoting the transition to the digital economy and low-carbon development.

The mechanism of renewable energy consumption, technological innovation and carbon productivity-an empirical study of Chinese data.

Renewable energy consumption has a strong impetus in promoting energy conservation and emission reduction, which is a new path leading to clean and low-carbon development. Based on that, this paper uses the data of carbon productivity, renewable energy power consumption level, technological progress, national economic development level, population, energy efficiency, industrial structure rationality, and other data in 30 provinces in China from 2011 to 2020, based on the STIRPAT model, conducts an empirical analysis on the impact of renewable energy power consumption on carbon productivity in Chinese provinces while considering both the spatial horizontal dimension and the temporal vertical dimension. The empirical results show that (1) Chinese carbon productivity presents an obvious spatial spillover effect and presents the spatial positive correlation distribution characteristics of "high-high" type agglomeration and "low-low" type agglomeration. (2) The utilization of renewable energy plays a positive role in promoting the development of low-carbon economy. The perspective of the horizontal spatial dimension shows a positive spatial spillover effect. The perspective of the longitudinal time dimension shows a marginal increase in the overall improvement of the environment. (3) Among the seven regions in China, the consumption of renewable energy in North China, East China, and Central China brings a dominant effect on carbon productivity. (4) About 29% of the positive effect of renewable energy consumption on carbon productivity is indirectly realized by technological progress. Finally, the article puts forward targeted policy suggestions.

Driving factors of spatial-temporal difference in China's transportation sector carbon productivity: an empirical analysis based on Geodetector method.

Carbon productivity is the core index to measure the performance of carbon emission reduction. Exploring the driving factors of the spatial-temporal differences in China's transportation sector, carbon productivity (TSCP) is conducive to the low-carbon sustainable development of the transportation sector. Based on the calculation of TSCP in 30 provinces in China from 2000 to 2019, we use time series, spatial visualization, and Dagum Gini coefficient to reveal the characteristics of spatial-temporal evolution and regional differences of TSCP, and uses Geodetector to identify the driving factors that affecting the spatial-temporal differences of TSCP. The results are as follows: (1) from 2000 to 2019, China's TSCP shows a U-shaped change trend of "decline to rise," and shows a spatial pattern of "high in the eastern and central, low in the western". (2) There are obvious regional differences in China's TSCP. The differences within each region show the trend of "eastern > central > western," while the differences between regions show the trend of "central-western > eastern-western > eastern-central," and the differences between regions are the main reason for the overall differences. (3) The spatial-temporal differences in China's TSCP are affected by many factors, such as social economy and self-endowment. Overall, energy intensity, foreign trade, technological innovation level, energy structure, and industrial structure are the dominant factors. Additionally, the interaction between the driving factors enhances the impact on the spatial-temporal differences of TSCP. Finally, according to the analysis results, some policy suggestions are put forward to improve TSCP.