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• This paper examines the effects of firm-level carbon productivity on volatility measures. • This paper uses U.S. firm-level carbon emission data and finds that carbon productivity is negatively associated with total and idiosyncratic volatilities. • We also show that the relationship is significant when the binding intergovernmental regulations such as Paris Agreement is active. • Findings hold under various robustness tests, including propensity score matching, coarsened exact matching and alternative risk variable tests. This study investigates the relationship between firm-level carbon productivity and volatility. With increasing interest in sustainable investing and inclusion of carbon productivity in financial assessments, we examine whether the market considers firms with high carbon productivity as less risky. Using U.S. firm-level carbon emission data, we find that carbon productivity is negatively associated with total and idiosyncratic volatilities. Our main findings hold under propensity score matching and coarsened exact matching. We also show that this relationship is significant when the binding intergovernmental regulations such as the Paris Agreement is active. [ABSTRACT FROM AUTHOR]

Digitalization is a driving force behind the ongoing energy industrial revolutions, catalyzing China's pursuit of carbon neutrality and sustainable development. Leveraging provincial data and annual reports from energy enterprises in China, this study constructs a comprehensive analytical framework that encompasses benchmark regression models, mediating effect models, threshold models, and spatial econometric models. These models are utilized to investigate the multi-faceted impacts of energy digitalization on carbon productivity (CP). The aim is to furnish micro-level evidence and policy guidance for advancing energy transformation and fostering low-carbon development enriched with digital elements. This research employs natural language processing and machine learning techniques to compute an Energy Digitalization Index, examining two critical dimensions: digital industry investment and the inclination toward digital transformation. The following key findings emerge: firstly, energy digitalization (ED) exhibits a statistically significant ability to enhance regional CP, a phenomenon marked by temporal and regional variations. Secondly, the analysis confirms the transmission mechanisms associated with energy technology innovation, energy structure, and energy utilization efficiency, as revealed through the Logarithmic Mean Divisia Index (LMDI) decomposition method. Furthermore, the optimal effect of energy digitalization on low-carbon economies materializes in settings characterized by mature market conditions, modest environmental regulations, advanced digital infrastructure, and reduced resource dependency. Additionally, the spatial Markov chain analysis unveils a conspicuous spatial distribution pattern termed "club convergence" in regional CP, accompanied by a pronounced "Matthew effect." According to the spatial Durbin model, energy digitalization generates favorable spatial spillover effects, primarily in peripheral regions, with a more pronounced short-term influence. Building upon these insights, this paper presents pertinent policy recommendations encompassing the national "digital energy" strategy, regional differentiation policies, and initiatives to stimulate digital technology innovation among enterprises. Our findings furnish robust empirical evidence and constructive policy insights, empowering governments to forge a smarter and cleaner energy ecosystem. Furthermore, these findings offer valuable guidance for other developing nations seeking to implement effective digital strategies. • The relationship between ED and CP is investigated from both theoretical and empirical perspectives. • ED is calculated from digital industry investment and the inclination toward digital transformation. • LMDI decomposition is applied to analyze the transmission mechanisms. • The complex nonlinear relationship between ED and CP is fully discussed. • The spatiotemporal transfer of CP is mined based on the spatial Markov chain model. [ABSTRACT FROM AUTHOR]

Abstract: Rapid economic development of industrial cities has led to a significant increase in energy consumption and carbon emissions in China. Through the log-mean Divisia index decomposition method and the cluster analysis method, this paper provides a systematic method to (1) analyze the time series of carbon emissions from the energy consumption of the industrial sector in a city; (2) explore the main factors affecting carbon emissions; and (3) divide industries in a city into different types and analyze combination features of “emissions–efficiency”. Finally, this paper takes Tianjin as an example, and studies the emissions characteristics of Tianjin. Results show that high-emission and low-efficiency industries play important roles in the development and economic growth of industries in Tianjin, improvements in energy utilization efficiency are the most important contributors to effective industrial energy conservation and emission reductions in Tianjin, and that the method developed in this paper is practical in the analysis of carbon emissions characteristics of an industrial city or region. [Copyright &y& Elsevier]

As urbanization has developed, a variety of urban clusters have gradually emerged with important effects on regional economic growth and carbon emissions. This paper constructs a multi-period difference-in-difference (DID) model for testing how urban cluster synergistic development policy (UCSDP) affects urban carbon productivity in 148 cities of China with 13 urban clusters as the experimental group from 2003 to 2019. According to the results, the UCSDP improves carbon productivity, mainly via reducing energy intensity. In addition, UCSDP improves carbon productivity by adjusting energy structure, increasing green innovation, altering city greening levels, and accelerating the process of urbanization. Moreover, it can produce synergistic effects in terms of energy structure, green innovation, greening level, and urbanization level. Furthermore, the UCSDP has heterogeneous effects on the carbon productivity in various cities. Specifically, the carbon productivity of high-status, high-rank, and resource-based cities responds more significantly to UCSDP. Therefore, policymakers should strengthen the integration of urban clusters, offer targeted development suggestions for different cities, modify the energy and greening structure, accelerate the process of urbanization, and focus on the formulation of reasonable environmental regulation policies. • Exploring the impact of UCSDP on carbon productivity with multi-period DID model. • The improvement of carbon productivity by UCSDP mainly comes from the decrease of energy intensity. • UCSDP increases carbon productivity by affecting energy structure, green innovation, greening level and urbanization level. • UCSDP produces synergistic effects in terms of energy structure, green innovation, greening level and urbanization level. • The impact of UCSDP on carbon productivity varies by city status, city rank and city type. [ABSTRACT FROM AUTHOR]

Renewable energy consumption has a strong driving force for promoting energy conservation and emission reduction, and is a new way to achieve clean and low-carbon development. Based on this, this paper uses the data of renewable energy consumption, carbon productivity, GDP, population, energy efficiency and technological innovation of 30 provinces in China from 2011 to 2020, and empirically analyzes the impact of renewable energy consumption on carbon productivity in 30 provinces in China, using STIRPAT model, comprehensively considers spatial horizontally and temporal vertically dimension. The empirical results show that: (1) China's carbon productivity shows a significant spatial spillover effect, its impact can spill over to adjacent areas within 1000 km; (2) China's renewable energy power consumption plays a significant role in promoting the improvement of carbon productivity. (3) Renewable energy consumption in the western region plays the most important role in driving carbon productivity. Finally, according to the temporal and spatial relationship between renewable energy consumption and carbon productivity in different regions, we provide specific regional policy suggestions. • The STIRPAT model is employed to explore temporal and spatial impact, while it considers sub regions. • Spatialy, the impact can spill over to adjacent areas within 1000km. Temporally, the impact fluctuates and rises. • The positive role of renewable energy consumption is found in China's western region. [ABSTRACT FROM AUTHOR]

Carbon productivity can integrate two goals of low-carbon economy, in the framework of low-carbon development, improving the carbon productivity has become the key strategy responses to the global extremely weather. This paper studies the level of low carbon development of China's eight major industries and uses Laspeyres index decomposition method to analysis the characteristics of China's carbon productivity change and the sources of industry structure from 2003 to 2011. Research results reveal that the structure factors hindered the growth of China's carbon productivity, and carbon productivity growth mainly comes from the industry production efficiency; agriculture and manufacturing have inhibitory effect on carbon productivity in the structure factors, while in the efficiency factors they have strong promoting impaction; other services in the structure factors play stronger roles in promoting carbon productivity growth. [ABSTRACT FROM AUTHOR]

whether political resource endowment affect regional low-carbon economic development has attracted the attention of many relevant stakeholders. Given that, by using remote sensing data such as nighttime light data and introducing the spatial econometric models, this paper selected 519 counties covering 69 prefecture-level cities in China's 14 provinces to explore the impact of political resource endowment on carbon productivity at the county level. The results showed that regional carbon productivity along the Long March areas was more 12% higher than that along non-Long March areas. Moreover, a series of robustness tests also verify that political resource endowment can effectively promote regional carbon productivity. Additionally, regional industrial structure is the most significant influencing factor in reducing carbon productivity. Our results also showed economic benefits generated from per unit carbon emissions in these counties along the Long March areas were obviously higher than those of counties along the non-Long March areas, indicating that counties along non-Long March areas should pay more attention to improving the balanced development of economic growth and environmental protection. Finally, for counties along the Long March areas and their surrounding regions, their carbon productivity spatially presents the typical spatial autocorrelation trend. Therefore, it is necessary to combine the transformation of industrial structure with the optimization of industrial spatial layout, and formulate the targeted improvement plan on carbon productivity for counties along the Long March areas to achieve coordinated low-carbon development. • The impact of political resource endowment on regional carbon productivity is explored; • Carbon productivity of counties along the Long March areas is higher than that of other counties; • Industrial structure hindered the growth of carbon productivity along the non-Long March areas; • Carbon productivity of counties benefitted from the endowment of political resources. [ABSTRACT FROM AUTHOR]

The rational implementation of environmental regulation and efforts to improve carbon productivity are the practical choices for China to address climate change. This paper mainly explores the correlations between environmental regulation and carbon productivity in China. The study first measures and analyzes carbon productivity and its changes. The results indicate that China's carbon productivity experienced several fluctuations during the study period. Overall, the carbon productivity is currently in a stable rising phase. According to the empirical research on the basis of the spatial spillover effect, a positive spatial correlation exists between carbon productivity in the different regions of China, and there is a threshold for the impact of environmental regulation on carbon productivity. As the intensity of environmental regulation changes from weak to strong, its effect on carbon productivity in local regions will be affected and change from negative to positive. Similar effects will be seen in neighboring regions. At present, the average intensity of China's environmental regulation has not yet reached this threshold. The eastern region's average intensity of environmental regulation is closest to its threshold, while the average intensities of the central and western regions are far from their respective thresholds. There is great potential to improve China's carbon productivity through the implementation of appropriate environmental regulations. • China's overall carbon productivity is currently in a stable rising phase. • China's carbon productivity has a significant positive spatial correlation. • The improvement of carbon productivity in local regions will also improve carbon productivity in neighboring regions. • There is a threshold for the impact of environmental regulation on carbon productivity in China. • The integral intensity of environmental regulation in China has not yet reached the threshold. [ABSTRACT FROM AUTHOR]