Improving Carbon Productivity Research Articles

The Impact of the Digital Economy on Provincial Carbon Productivity: Empirical Evidence from China

With the introduction of the dual carbon goals of “carbon peak” and “carbon neutrality”, low-carbon economic development has become the focus of attention for all sectors in China. Improving carbon productivity (CP) is the key to developing a low-carbon economy and is essential to achieving carbon emission reduction and economic growth. The rapid development of the digital economy (DE) provides a new breakthrough for improving carbon productivity, but there are few studies on the digital economy and carbon productivity in the existing literatures. We empirically investigate the impact mechanism of the digital economy on provincial carbon productivity using China’s provincial panel data from 2010 to 2019. This study finds that the digital economy significantly raises provincial carbon productivity; this result remains valid after a series of robustness tests. The digital economy can impact provincial carbon productivity through both energy consumption structure (ECS) and green technology innovation (GTI) and can contribute to the improvement of provincial carbon productivity by optimizing energy consumption structure and improving green technology innovation. In addition, different environmental regulations have different impacts. Command-and-control environmental regulation (CER) weakens the positive impact of the digital economy on provincial carbon productivity, whereas market-based environmental regulation (MER) enhances the positive impact of the digital economy on provincial carbon productivity. This study extends the research on the digital economy in the field of low-carbon development, providing insights and policy suggestions for relevant departments to promote low-carbon development from the perspective of the digital economy.

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.

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.

The Role of Industrial Structure in the Impact of Environmental Regulation on Carbon Productivity

In the context of global climate change, China has proposed the "Carbon peaking and Carbon neutrality goals" goal of carbon emissions will reach carbon peak by 2030 and carbon neutral by 2060. As a market-based environmental regulation policy to promote greenhouse gas emission reduction, carbon emission trading policy is expected to reduce carbon emissions and improve carbon productivity, among which industrial structure plays a regulatory role in the impact of carbon emission trading policy on carbon productivity. Using the provincial data of China from 2006 to 2019, this paper evaluated the influence of carbon emission trading policy on carbon emission, taking the industrial structure as the regulating variable, studied the industrial structure in the influence of environmental regulation on carbon emission, and tested the regression results of DID model through parallel trend test, placebo test and PSM-DID model regression. After the above research process, the following conclusions are drawn: the carbon emission trading policy has a significant positive promotion effect on carbon emission reduction, and it is further concluded that the industrial structure has a significant negative regulatory effect on carbon productivity under the carbon emission trading policy.

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.

The severity of foreign direct investment components on China's carbon productivity

Carbon dioxide emission reduction is one of the major pressures on China's current economic advancement, and the improvement of carbon productivity (CP) has become particularly relevant. Promoting sustainable foreign direct investment (FDI) can help improve carbon productivity. As a result, the need to assess the severity of FDI components on sustainable economic growth as the world economy interoperates and expands is imperative. Using 30 Chinese provinces, the study investigates the impact of FDI components on China's CP from 2006 to 2020. The spatial model is utilized in this study to assess the interactive influence of these components on CP. It was found that, while CP is significantly influenced by inbound FDI (IFDI), its spatial spillover impact is not significant. Conversely, outbound FDI (OFDI) negatively affects local CP, as well as significantly reduces CP through spatial spillover in nearby regions. Also, the developmental coordination of IFDI and OFDI significantly enhances the country's CP, primarily due to positive spatial spillover effects. There exists a regional heterogeneity exhibited by the influence of both IFDI and OFDI on CP. In the eastern region, the IFDI has a negative impact on CP. However, OFDI possesses a positive effect on CP in the eastern region and an adverse influence in the western, southern, and northern regions. These results together with the policy implications will provide great insights into encouraging green investment from foreign countries.

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 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.

Measurement and driving factors of carbon productivity in China's provinces: From the perspective of embodied carbon emissions.

Carbon productivity incorporates economic development and carbon emissions within a unified framework for measuring the economic value per unit carbon emissions. In the context of climate change, improving carbon productivity is of great value for promoting low-carbon development in a country or region. From the perspective of embodied carbon emissions, this study constructs an embodied carbon productivity (ECP) index and uses the Logarithmic Mean Divisia Index decomposition method to study the evolution trends and driving factors of ECP in China's provinces based on China Interregional Input-Output Tables for 2002, 2007, 2012, and 2017. The following results were obtained: First, China's overall ECP showed a continuously increasing trend during the entire period, with the energy efficiency factor playing the largest role among all driving factors. Second, the ECP in 19 of the 30 Chinese provinces continued to increase and the contributions of energy emission ratio, ECP per capita, and population size factors to the increase in ECP presented evident disparities among different provinces. Third, the ECP in three major regions ranged from high to low in the order of East, Central, and West, with the largest growth in the Central, followed by the West, with the smallest in the East. Based on the analysis of research results, we proposed relevant policy recommendations to further improve China's ECP and achieve low-carbon economy.

The effect of innovation pilot on carbon total factor productivity: Quasi-experimental evidence from China

Since the policy design for carbon emission reduction must consider both the efficiency and the cost of emission reduction, improvement in carbon total factor productivity (CTFP) is widely regarded as an important economic path in reducing carbon emissions. With prefecture-level data in China from 2003 to 2016, this paper explores the role of innovation pilot on the CTFP using China's Innovative City Pilot (ICP) policy as a quasi-experiment. First, we construct a novel CTFP framework, that combines the non-parametric Luenberger productivity indicator with the biennial non-radial directional distance function, to take the slack issues and infeasibility problem into consideration. Next, with the staggered difference-in-differences model, we examine whether innovation can stimulate CTFP improvement. The results show that the ICP policy exerts a significant positive effect on CTFP, increasing cities' CTFP by 0.015 on average. Moreover, we identify five potential channels from both economic analysis and comprehensive factor decomposition: technological and low-carbon innovation, industrial upgrading, energy efficiency, carbon emission intensity, and efficiency change. These positive impacts are heterogeneous across different kinds of cities. Specifically, the effects of innovation are stronger in the eastern and central cities, large cities, and cities with tighter environmental regulations. Meanwhile, the political incentives analysis shows that in cities with higher political incentives, officials perform better in energy intensity reduction and CTFP improvement. This paper explores direct evidence for the innovation's effect on carbon productivity, which provides a novel perspective for improving carbon productivity and the future direction of innovation in China.

Spatial impact of digital finance on carbon productivity

Low carbon productivity has been identified as a key direction for China’s future development. As an important driving force for economic growth, the question of whether digital finance that is reliant on digital technology can support the development of a low-carbon urban economy remains unresolved. Based on the carbon productivity measured by panel data from 201 cities for the period 2011–2020, this study applies the spatial Dubin model and threshold regression model to explore the impact of digital finance on carbon productivity, yielding the following key conclusions. First, the spatial distribution heterogeneity of carbon productivity in China’s eastern region is higher than that in the western region, and both productivity and digital finance are characterized by high (low)–high (low) dotted spatial agglomeration. Second, digital finance can significantly improve carbon productivity via two transmission channels: the human capital and marketization effects. At the same time, digital finance exerts a spatial spillover effect on carbon productivity, and rising local digital finance levels will increase carbon productivity in neighboring areas. Heterogeneity analysis indicates that the spillover effect of digital finance in urban agglomerations and eastern regions is more significant. Third, fixed-asset investment has a positive nonlinear moderating effect on digital finance, thus improving carbon productivity. When the per capita investment in fixed assets does not exceed 682.73 yuan, digital finance exerts only a limit pulling effect on carbon productivity; when it is higher than this value, the pulling effect is intensified.

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.

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.

Exploring the role of co-agglomeration of manufacturing and producer services on carbon productivity: An empirical study of 282 cities in China

Carbon productivity is a better measure of a region's sustainable economic growth since it considers economic development and carbon emissions at the same time. It is crucial to improve carbon productivity through the co-agglomeration of manufacturing and producer services for China. To better understand the effect and mechanism of co-agglomeration, this study uses panel data of 282 Chinese cities from 2003 to 2017 to examine the effect of manufacturing and producer services co-agglomeration on carbon productivity. Further, moderating effects models and panel threshold models are used to investigate the mechanisms of co-agglomeration effect on carbon productivity. The results suggest that the influence of co-agglomeration on carbon productivity is U-shaped with an inflection point of 3.63, indicating that a high level of co-agglomeration is required for carbon productivity to increase. Compared with other cities, cities in the eastern region or with a large scale have lower inflection points. The upgrading of industrial structure, production efficiency, and the production chain has a positive moderating effect on the influence of co-agglomeration on carbon productivity, while upgrading of non-production chains does not. When the levels of capital misallocation and labor misallocation reach the threshold of 1.115 and 1.557 respectively, industrial co-agglomeration has a promoting effect on carbon productivity. This research offers valuable insights for optimizing the urban industrial layout to improve carbon productivity.

Can smart city construction improve carbon productivity? —A quasi-natural experiment based on China's smart city pilot

At present, China is facing the dilemma of how to achieve the goal of carbon emission reduction under the premise of ensuring economic growth. As a new urban development model, smart city construction may become the key to solving this problem. Based on a literature review on smart city construction and carbon emissions, this study uses a multi-period difference-in-difference model for empirical analysis, aiming to assess the impact of smart city pilot policy on carbon productivity. We find that: (1) Smart city construction has increased urban carbon productivity by 2.045%. Among them, the promotion effect of eastern and central cities is noticeable, while western cities have a restraining effect. (2) The impact of smart city construction on carbon productivity has a time-lag effect. The pilot policy will take effect in the third year of implementation and reach its maximum value in the fifth year. (3) Smart city construction can improve urban carbon productivity through technological progress, industrial structure upgrading, and energy structure optimization. Therefore, it is recommended to formulate policies from collaborative innovation between cities, optimizing industrial structure, and accelerating renewable energy replacement to achieve sustainable economic development.

Impact of carbon productivity on firm performance: moderating role of industry type and firm size

PurposeThe primary purpose of this study is to investigate the impact of carbon productivity on firms' financial performance. Secondly, the study also examines the moderating effect of industry types and firm size in the relationship between productivity and firm performance.Design/methodology/approachThe data used for the study includes 66 listed Indian firms over the period from 2015–2016 to 2019–2020. The data used in the study are collected from the published corporate annual reports and sustainability reports. The study uses a random effect model based on the results of the Hausman test and the Breusch-Pagan test to investigate its objectives.FindingsCarbon productivity has a favorable impact on firms' financial performance in India, indicating that firms may gain competitive advantages by minimizing carbon emissions and improving carbon productivity. Small and high carbon-intensive firms reap greater benefits from the improvement in carbon productivity compared to their opposite counterparts. However, such differential impact is only observed for the market-based measure but not for the accounting-based measure of financial performance.Practical implicationsThe results suggest that high carbon-intensive firms should focus more on improving carbon productivity. Small firms and firms belonging to high carbon-intensive industries can improve their market performance by improving carbon productivity.Originality/valueThis study is a noble attempt to investigate the moderating effect of industry type and firm size while examining the impact of carbon productivity on firm performance in the context of an emerging economy.

The impact of clean energy development finance and financial agglomeration on carbon productivity in Africa

The growing threats of climate change are posing severe economic and social development challenges to African economies. As a result, emission reduction path that stabilizes carbon concentrations while minimizing the risk of damage from rising climate threats has become critical. The key is to improve carbon productivity. In this regard, the study shed light on the direction and magnitude of clean energy development finance (CEDF) and financial agglomeration (FA) impact on dynamic total carbon productivity index (DTCPI). Based on panel data of 35 economies in Africa from 2005 to 2017, we employ level of development and fossil fuel intensity as threshold variables to explore the relationship between CEDF and DTCPI using panel threshold technique. Similarly, we examined FA and DTCPI nexus under level of development. The DTCPI was estimated using Parametric Malmquist Index technique. The findings indicate that, on average, DTCPI progressed over the sample period and technical efficiency is the central factor behind the improvement. Besides, a threshold effect exists between FA, CEDF, and DTCPI. Particularly, the negative effect of CEDF on carbon productivity increases at a certain level and subsequently reduces as economic development advances. Also, CEDF exhibits a significant and greater decline on carbon productivity only at high fossil intensity levels. The negative effect of FA on DTCPI contracts as economic growth increases and remains high in low income levels. The study encourages governments to scale up clean investment initiatives, guide the formation of agglomerations, and enhance innovation while pursuing sustainable growth.

Low-carbon city pilot policy, fiscal pressure, and carbon productivity: Evidence from china

The implementation of the low-carbon city pilot policy is an important measure to reduce carbon emissions and promote low-carbon economic development in China. However, the resulting fiscal pressure may be counterproductive. The aim of this paper is to investigate the impacts of the low-carbon city pilot policy and fiscal pressure on carbon productivity. Based on the data of 282 cities in China over the period 2005 to 2017, this paper uses the staggered difference-in-differences (DID) model to identify the causal relationship among the low-carbon city pilot policy, fiscal pressure, and carbon productivity. The results show that this pilot policy can significantly improve carbon productivity and that the improvement effect presents a dynamic and persistent feature. However, the fiscal pressure resulting from this pilot policy can reduce carbon productivity, and the degree of reduction depends on the status of fiscal pressure. Increased fiscal pressure has a negative impact on carbon productivity, which is heterogeneous with different levels of economic development. Moreover, the mediation effect analysis finds that this pilot policy affects carbon productivity by adjusting the energy production and consumption structure, enhancing green technology innovation capabilities, and increasing the number of low-carbon-type enterprises entering the market. This paper provides new ideas for improving carbon productivity without increasing fiscal pressure. It also recommends that fiscal pressure cannot be ignored in the implementation of the low-carbon city pilot policy.

The Digital Economy and Carbon Productivity: Evidence at China’s City Level

Based on the panel data of 285 prefecture-level cities in China, this paper empirically tests the impact of digital economic development on carbon productivity by using a two-way fixed effect model, intermediary mechanism model and threshold mechanism model. The results show that: (1) the digital economy can significantly improve carbon productivity, and this conclusion is still valid after a series of robustness tests. (2) An intermediary mechanism test found that technological innovation, reducing energy consumption intensity and improving urban productivity are the three primary paths through which the digital economy significantly improves carbon productivity. (3) A threshold mechanism test found that the promotion effect of the digital economy on carbon productivity is also affected by the degree of marketization and the level of human capital, showing a single threshold effect and a U-shaped trend. (4) The impact of the digital economy on carbon productivity has regional heterogeneity, urban agglomeration heterogeneity, and resource-based city heterogeneity. This study provides substantial empirical evidence for the relevant authorities to formulate green development policies from the perspective of digital economy development.

Foreign direct investment entry mode and China’s carbon productivity based on spatial econometric model

As the world’s largest developing country and the largest carbon emitter, China must consider economic growth and carbon emission reduction in development. Therefore, improving carbon productivity is an important goal of China at present. At the same time, China’s foreign capital inflow has always been at the forefront of the world, and foreign direct investment (FDI) has had various impacts on China’s carbon productivity. Based on the panel data of 25 provinces in China from 2007 to 2019, this paper uses a spatial econometric model to study the difference in the impact of FDI on China’s carbon productivity under different entry modes. The study found that: when FDI enters China in the mode of joint ventures, there is a positive spatial spillover effect, which is conducive to improving China’s carbon productivity; while when FDI enters China in the mode of wholly foreign-owned enterprises, there is a negative spatial spillover effect, which will inhibit the improvement of China’s carbon productivity. Therefore, when introducing foreign capital, the Chinese government should formulate differentiated foreign investment policies according to the different entry modes of FDI, and encourage more FDI to enter China in the mode of joint ventures.