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.

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.

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.

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.

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.

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.

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 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.

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.

The impact of depth of environmental provisions and CO2 emissions embodied in international trade

The construction of trade power and green low-carbon transformation are common high-quality development goals for countries worldwide. The depth of the environmental provisions contained in different countries' intercountry trade agreements and the implicit carbon data of intercountry trade are accurately measured based on the textual analysis of trade agreement rules, and it is empirically found that enhancing the depth of the environmental provisions contained in trade agreements can significantly reduce CO2 emissions embodied in international trade. The capacity of intercountry green technology cooperation to strengthen the internal environmental governance capacity of countries and to enhance carbon productivity is an important transmission mechanism. The effect of the environmental provisions in different types of trade agreements on reducing trade-implied carbon is obviously heterogeneous; the higher the level of correlation with carbon emissions is, the stronger the effect of trade agreement provisions, and that effect is more significant in developed countries and in the reduction of foreign carbon emissions that are embedded in export products. In this paper, the impact of the depth of the environmental provisions of trade agreements on trade-implied carbon under the same framework is directly studied, which not only advances the measurement of the depth of the environmental provisions of trade agreements and expands the research field on the scope of influence of existing trade agreements but also fully accounts for the role of the effectiveness of environmental provisions in different contexts and provides a theoretical basis for the optimization of future environmental provisions.

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 β spatial convergence and conditional β 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.

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.

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.

Can using energy resources productively and promoting good governance boost carbon productivity? An economic growth-environmental degradation decoupling analysis on 116 global countries

Decoupling economic growth from environmental pollution for promoting low-carbon growth has become a global objective. Though the previous studies have mostly analyzed how environmental pollution can be reduced, not much emphasis was given to assessing how economic growth can be enhanced while limiting environmental damages in tandem. Hence, this study examines how carbon productivity is determined by energy productivity improvement, good governance, financial development, financial globalization, and international trade using data from 116 global economies. Overall, the analytical findings reveal that energy productivity improvement initially cannot decouple economic growth from environmental pollution by inhibiting carbon productivity. However, later on, using energy productively does manage to decouple economic growth from environmental pollution by boosting carbon productivity. Accordingly, the U-shaped nexus between these variables is confirmed by these statistical findings. Besides, the results also endorse the carbon productivity-boosting effects of good governance, financial development, and international trade while foreign direct investment receipts are not found to exert any significant impact on carbon productivity. On the other hand, the robustness tests' results affirm that the carbon productivity-influencing impacts are heterogeneous across countries belonging from different categories of national income, carbon productivity, energy productivity, governance, and regional locations, as well. Nevertheless, the results overall confirm that countries having comparatively higher levels of energy productivity and governance are more likely to decouple the growth of their respective economies from environmental pollution. Based on these findings, some decoupling policies are recommended.

High-speed rail and city's carbon productivity in China: a spatial difference-in-differences approach

Improving carbon productivity is an important measure to promote low-carbon development. Since high-speed rail (HSR) has both economic and environmental effects, it is particularly important to clarify the relationship between HSR development and carbon productivity. In this paper, 285 cities in China from 2007 to 2017 are used as a research sample, and the relationship between the opening of HSR and the city's carbon productivity is studied using the spatial difference-in-difference method (SDID). The result shows that due to the intermediary effect of technological innovation and industrial structure, the opening of HSR significantly increases urban carbon productivity. At the same time, this influence has a significant positive spatial spillover effect. On average, when a city opens HSR, the local carbon productivity increases by 5.18%, and the carbon productivity of its neighboring cities increases by 13.52%. Overall, the positive effect of HSR on carbon productivity is more pronounced in the middle and western regions. However, the spatial spillover effect in the eastern region is significantly negative. These findings help to accurately assess the social benefits of HSR network expansion and provide important decision-making references for climate governance in the HSR era.

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.

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 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.

Spatiotemporal pattern of carbon productivity and carbon offset potential in Chinese counties

This study used panel data for 2166 county-level units in China between 2000 and 2017 to characterize carbon productivity's temporal and spatial evolution in Chinese counties. The "catch-up effect" of carbon offsets between counties was examined, and this was used to measure the carbon offset potential and analyze its spatial differences. The results indicated that: (1) China's carbon productivity increased from 2000 to 2017; carbon productivity in eastern China was the highest, and carbon productivity in northeast China was the lowest. A polarization to unipolarization evolutionary trend was evident. (2) Considering the spatial differences in carbon productivity, the overall difference in carbon productivity in China from 2000 to 2017 first decreased and then increased; the contribution rate of differences between cities and counties to the overall difference was relatively stable, at around 39.88 %. The contribution rate of the differences between zones to the overall difference was the lowest, remaining stable at around 2.49 %. (3) In terms of the spatial distribution of the carbon offset potential, the eastern region exhibited the highest average carbon offset potential. The carbon offset potential of China exhibited a "high in the north and low in the south; low in the west and high in the east" spatial pattern.

How does power technology innovation affect carbon productivity? A spatial perspective in China

Power technology innovation has been positioned as an effective way to contribute to China's carbon productivity. However, limited empirical evidence exists on the impact of power technology innovation on carbon productivity. Thus, based on the annual panel dataset of 30 China's provinces from 2001 to 2019, this study explored whether and how power technology innovation promotes or impedes the improvement of carbon productivity. First, carbon productivity in the framework of total factor was calculated based on the metafrontier Malmquist-Luenberger productivity index. Second, the effect of power technology innovation on carbon productivity was investigated using the spatial Durbin model. And we also examined whether heterogeneous power technology innovations have a synergistic effect on carbon productivity. Third, influence mechanism of power technology innovation affecting carbon productivity was identified. Results show that (1) there are notable differences in China's provincial carbon productivity, which is characterized by the spatial correlation. (2) Local power technology innovation has a promotion effect on carbon productivity in both local and neighboring provinces. Moreover, the promotion effect of breakthrough power technology innovation is stronger than that of incremental power technology innovation. (3) Catching-up Effect and Innovation Effect are important transmission channels through which power technology innovation improves carbon productivity. Finally, policy recommendations are provided.

Heterogeneous two-sided effects of different types of environmental regulations on carbon productivity in China

In the context of globalization, the importance of enhancing carbon productivity is becoming increasingly evident. The study is a continuation of previous studies on the relationship between environmental regulations and carbon productivity. Based on a dataset of 30 provinces in China from 2006 to 2018, the paper decomposes the two-sided effects of command-and-control and market-based environmental regulations on carbon productivity. First, empirical research shows that the average positive effect of command-and-control environmental regulation on carbon productivity is 0.0158, which is much less than the average of negative effect of 0.0697, highlighting mainly the negative effect on carbon productivity. Conversely, the positive effect of market-based environmental regulation on carbon productivity is 0.0691, much greater than the negative effect of 0.0038, which highlights the obvious positive impact characteristics. Overall, the net effect of command-and-control environmental regulation on carbon productivity is -0.0541, and net effect of market-based environmental regulation on carbon productivity is 0.0653. Second, the negative impact of command-and-control environmental regulations on carbon productivity underwent a "back-to-N" change process in 2006-2018, while the driving effect of market-based environmental regulation on carbon productivity increased continuously during the 2006-2018 period. Third, most of the regions with high negative effects of command-and-control environmental regulation on carbon productivity tend to be resource-intensive and carbon-intensive, while the positive effects of market-based environmental regulation on carbon productivity have no obvious geographical agglomeration characteristics. Fourth, the continuous improvement of regional development conditions is clearly conducive to the continuous reduction of the negative effects of command-and-control environmental regulation on carbon productivity, while the higher positive effects of market-based environmental regulation on carbon productivity at this stage need to meet different regional condition intervals.

Threshold effect of industrial agglomeration on carbon productivity in China's Yangtze River economic belt: a perspective of technical resourcing

The Yangtze River Economic Belt's industrial layout is characterized by industrial agglomeration. However, industrial agglomeration, while promoting economic development, has an uncertain impact on the ecological environment. This research studies the threshold impacts of pollution-intensive industrial agglomeration and green-based industrial agglomeration on the carbon productivity of the Yangtze River Economic Belt through the panel threshold regression models to find the "optimal industrial agglomeration scale." The results of the "optimal industrial agglomeration scale" show that under the existing economic conditions, only if pollution-intensive industrial agglomeration is controlled within a reasonable range can it contribute to carbon productivity. Green-based industries can only enhance carbon productivity when the scale of agglomeration reaches a certain value. In addition, this paper also points out that along the Yangtze River Economic Belt, regions with high agglomeration of green industries should consider investing more technological resources in emerging technologies that use clean energy as a production condition. In contrast, regions with high agglomeration of pollution-intensive industries should focus on improving existing technologies in which traditional energy sources are used as production conditions to increased carbon productivity.

Pathways to securing environmentally sustainable economic growth through efficient use of energy: a bootstrapped ARDL analysis

Oman has traditionally relied upon natural gas and oil for meeting its domestic energy demand. As a result, despite growing economically, the level of carbon dioxide emissions in Oman has persistently surged; consequently, the nation has failed to ensure environmentally sustainable economic growth. Against this background, this current study aims to explore the impacts of energy consumption, energy efficiency, and financial development on Oman's prospects of attaining environmentally sustainable growth over the 1972-2019 period. The estimation strategy is designed to take into account the structural break issues in the data. Using the carbon productivity level as an indicator of environmentally sustainable economic growth, we find long-run associations amid the study variables. Besides, higher energy consumption and greater financial development are found to impede carbon productivity while improving energy efficiency is observed to boost carbon productivity in Oman. Therefore, it is pertinent for Oman to consume low-carbon and energy-efficient fossil fuels, improve energy efficiency levels, and green its financial sector to achieve environmentally sustainable growth.

Research on carbon productivity and its spatial convergence of steel industry in China

The Global-Malmquist-Luenberger (GML) index was applied to analyze the carbon productivity in steel industry (SICP) of 29 provinces in China from 2006 to 2019, and then, the SICP was decomposed into technical efficiency change index (TC) and technical progress index (EC). On this basis, the spatial effect is introduced into the traditional convergence model to investigate the spatial convergence of SICP. The empirical results show that: (1) the overall carbon productivity of China's steel industry is at a relatively low level, showing a slow growth trend. (2) The average value of the GML index of SICP is higher than 1, showing obvious inter-provincial and regional heterogeneity. Compared with TC, EC is the leading factor that promotes the increase of SICP. (3) The spatial absolute and condition β convergence of SICP exist in the whole country and the three major regions, but the σ convergence feature is not significant. The addition of spatial factors speeds up the convergence trend, and the speed of spatial absolute β convergence is about 3 times that of the classical convergence model. At the same time, the conditional convergence rate is significantly faster than the absolute convergence, which is closely related to the differences in influencing factors such as the industrial structure, economic development level, human capital, energy consumption intensity, and R&D investment among regions. There is still much room for improvement in carbon productivity in China's steel industry, and investment in scientific research must be increased in order to achieve the upgrading of the industrial structure and technological innovation. The existence of spatial convergence requires strengthening the joint reorganization of steel enterprises between provinces and regions, making full use of the spatial spillover effects of production technology, and realizing regional green and coordinated development.

On green credits and carbon productivity in China

Based on panel data from 30 provinces over the period of 2003-2016, this study uses the spatial econometric model to examine the effect of green credits on carbon productivity. The research findings show that there is a significant positive correlation between green credits and carbon productivity among provinces during this period. Provinces with high levels of carbon productivity (green credits) are also geographically adjacent or economically close to provinces with high levels and vice versa. Regression results of the whole sample show that green credits not only promote carbon productivity, but also have a positive spatial spillover effect. Similar regression results using regional sub-samples indicate that the direct promotion effect and spatial spillover effect of green credits on carbon productivity are more obvious in the central and western regions than in the eastern parts of the country. The research findings have important and relevant policy implications as far as the relationship between green credits and carbon productivity is concerned.

A step towards sustainable path: The effect of globalization on China's carbon productivity from panel threshold approach

Surfacing the stress of global CO₂ emission reduction and the change into a low-emission economy has become one of the prominent economic concerns in the twenty-first century. The essence of evolving a low-emission economy is to raise carbon productivity that can be estimated as the cost-effective paybacks of CO₂ emissions. A panel threshold model was applied to approximate the threshold effect of globalization on carbon productivity under the development of human capital by using the panel data of thirty provinces of China from 2009 to 2017. The empirical findings demonstrate that China's carbon productivity increases, while economic growth shape moves towards sustainable development with low-carbon emission. Moreover, the driving force of globalization on carbon productivity is not tediously decreasing/increasing, but it has a double threshold effect of human capital. In line with this, this study finding found a single and double threshold of 9.3478 and 10.8800, respectively, as a benchmark where the relationship turns positive. The empirical findings have suggested several policy implications for the Chinese Government, policymakers, and regulatory authorities regarding this critical issue.

Role of low-carbon technology innovation in environmental performance of manufacturing: evidence from OECD countries

Climate change disrupts the balance of natural ecosystems and threatens the sustainable development of human society. As the leading industry in many countries, manufacturing promotes economic growth; unfortunately, it also emits large quantities of greenhouse gases. Thus, it is necessary to transform the production pattern of manufacturing into green production. Although technology innovation is the only way to tackle the issue, different types of technology innovation may lead to various environmental performances. We argue that low-carbon technology innovation (LCTI) is the key to green production. Using data of Economic Co-operation and Development (OECD) countries from 1990 to 2014, we use the patent-stock method to measure LCTI levels and analyze its development trend in OECD countries. Based on the shepherd distance function, we measure carbon efficiency and carbon productivity by the fixed-effect Stochastic Frontier Analysis (SFA) model. Then we investigate the effect of LCTI on carbon emission efficiency in manufacturing by the fixed-effect regression model. After controlling some variables, evidence shows a significant positive influence of LCTI on the environmental performance of manufacturing. The level of LCTI constantly increased in OECD countries during the study period. Among these countries, the level of low-carbon technology in the chemical industry is the highest; in most countries, the low-carbon technology of the production process grows fastest. Policy implications are further discussed.

Do financial regulations matters for sustainable green economy: evidence from Turkey

The attention of scholars and policymakers on the achievement of sustainable green economy has been on increase; however, the topic has not been exhaustively investigated. This study empirically investigates the implications of financial regulations on sustainable green economy in Turkey utilizing a time series data spanning from 1996 to 2019. This study employed Perron and Lee-Strazicich unit root test in the presence of structural break point for examining the stationarity properties of the series and FMOLS, CCR, and ARDL for estimating the long and short-run effect of the financial regulations on carbon productivity. Our study demonstrates that rule of law, economic freedom, and inflation have a significant long-run relationship with carbon productivity as confirmed by FMOLS and CCR, while rule of law, regulatory quality economic freedom, and inflation were confirmed by ARDL to have long-run causal relationship with carbon productivity. In addition, our study found that control of corruption, government effectiveness, rule of law, regulatory quality economic freedom, and inflation have a short-run causal effect on carbon productivity. Finally, this study concludes that financial regulations is significant for achieving sustainable green economy in Turkey and as such should be accorded adequate attention by the policy makers.

The evolution characteristics and influence factors of carbon productivity in China's industrial sector: from the perspective of embodied carbon emissions

In the context of low-carbon economic development, carbon productivity has effectively integrated the two major objectives of carbon reduction and economic growth, and increasing carbon productivity has become the main approach to address global climate change. Using data from China's input-output table for 2002-2017, this study measures the evolutionary characteristics and influencing factors of carbon productivity from the perspective of embodied carbon emission in China's industrial sector. The results indicate that, first, China's industrial sectors' carbon productivity shows an increasing trend from 25.22 to 65.3 million yuan/10,000 tons of CO₂ in 2002-2017, but the overall level is low. The energy efficiency factor is a major element affecting improvement in carbon productivity. Second, the carbon productivity of the 28 industrial sectors shows an increasing trend. There is a significant gap in the carbon productivity between different industrial sectors. The energy efficiency factor in all industrial sectors is positive, indicating that energy efficiency is a positive factor in increasing carbon productivity. Third, from largest to smallest in the carbon productivity are the primary, tertiary, and secondary industries. Finally, this paper recommends several approaches to improve carbon productivity from the perspectives of technology, differentiated policies of carbon emission reduction, and industrial structure adjustment.

Examining the effects of socioeconomic development on China's carbon productivity: A panel data analysis

China, which is the largest carbon emitter and the largest developing country in the world, faces the challenge of achieving energy conservation and emission reduction without sacrificing economic development. Improving carbon productivity consists a possible way to seek a coordination between economic development and carbon emission reduction. Therefore, it is of great significance to examine the effects of socioeconomic development on China's carbon productivity and accordingly provide policy suggestions for China's low-carbon economic development. However, this topic has not been adequately addressed in previous studies. In order to fill this gap, this study detailed an empirical investigation into the impacts of socioeconomic development on China's carbon productivity. First, aided by spatial analysis methods, a detailed analysis of the spatiotemporal patterns and dynamics of China's province-level carbon productivity was conducted. Moreover, using an extended STIRPAT model and panel data modeling technique, the effects of a range of socioeconomic factors on China's carbon productivity were quantitatively examined. The results indicated that China's carbon productivity increased gradually between 1997 and 2016, and carbon productivity in East China was much higher than that of their counterparts in Central China and West China. Provincial administrative units with highly developed economies witnessed spectacular increases in carbon productivity. Panel data analysis demonstrated that GDP per capita, technology level, trade openness, and foreign direct investment exerted positive effects, while energy consumption structure, industrial proportion, and urbanization level exerted negative effects, on China's carbon productivity. Based on the findings of this study, a series of policy suggestions with respect to improving China's carbon productivity were proposed.