Does firm carbon risk matter to banks? Evidence from the US syndicated loan market.

Using two measures of firm carbon-risk exposure to capture convergent materiality, we examine whether the syndicated loan market considers borrowers' carbon-risk exposure in loan pricing. We find that carbon intensity does not constitute material information for banks, whereas environmental scores have a statistically significant but incidental economic effect on loan spreads. We also find inconclusive evidence that the market differentiates between firms in high and low environmentally sensitive industries. Finally, we show that green banks charge higher loan spreads to more environmentally responsible firms. Overall, we provide strong evidence that environmental scores matter in loan pricing decisions.

Does innovative city pilot policy improve carbon reduction? Quasi-experimental evidence from China.

A new direction for China in exploring sustainable development is the Innovative City Pilot Policy (ICPP), which provides policy guidance for accelerating carbon peaking and carbon neutrality by reducing carbon emissions. With data from 282 cities spanning 2006-2018, this paper examines ICPP's effect on carbon intensity (CI) through a multi-period difference-in-differences (DID) model, as well as exploring the mediating effect, moderating effect, heterogeneity, and spatial spillover effects. The results show that ICPP reduces CI significantly by enhancing technology innovation (TI), and when industrial structure (IS) is added, the effect of ICPP is expanded. The ICPP gains additional advantages in reducing CI by optimizing the efficiency of resource allocation (ERA). Compared with the concentration of human capital (HCL), the amount of scientific research institutes (SRI) has a slightly greater moderating effect. ICPP impacts considering location, size, and hierarchy heterogeneity. ICPP has a greater impact on mitigating CI in the western, larger size, and provincial capital cities. There are positive spillover effects of the ICPP on neighboring CI. To support the idea that ICPP can effectively contribute to CI reduction, this paper provides empirical evidence and theoretical guidelines.

Utilizing green finance to promote low-carbon transition of Chinese cities: insights from technological innovation and industrial structure adjustment.

Green finance (GF) has emerged as a promising tool to promote low-carbon development, while knowledge is rather limited regarding the underlying mechanism. This article aims to address this void by constructing a city-level GF index covering seven dimensions and identifying the main pathways through which GF can facilitate the low-carbon development of cities. Using a balanced panel data covering 277 Chinese cities from 2010 to 2020, the results show that: (1) China's GF development exhibits an overall spatial differentiation of 'high in the east and low in the west', while the distribution of carbon intensity (CI) displays an overall spatial differentiation of 'high in the north and low in the south'; (2) GF significantly decreases CI of cities, which is robust to employing DID strategies and IV estimations; (3) The role of GF on CI varies with the level of CI whereas not with the level of GF. Specifically, the mitigating effect of GF on CI is significant in both high GF and low GF groups, but only in high CI group; and (4) GF promotes low-carbon transition of cities through mainly on adjusting industrial structure rather than stimulating technological innovation. Despite we also demonstrate green finance enhances green innovation, due to multi-factors, such technology progress it brings may not always translate into a tangible improvement in green productivity. For most developing countries including China, the future policy objective of green finance should focus on enhancing sustainable technological progress.

Digital infrastructure expansion and carbon intensity of small enterprises: Evidence from China.

While the growth of digital infrastructure theoretically offers increased opportunities for enterprises, the policy implications may vary depending on the firm size. We utilize the pilot policy of the Broadband China Strategy as an exogenous shock for firms and adopt a unique dataset from the National Tax Survey to investigate the impact of digital infrastructure expansion on the carbon intensity of small enterprises. The results indicate that digital infrastructure expansion leads to a notable increase of 9.04% in small firm's carbon intensity. These results exhibit resilience, withstanding rigorous testing through various robustness checks. This increase is primarily attributed to two channels: the competition effect, which results in a decline in small firm's output, and the change in energy structure, which leads to an increase in small firm's carbon emissions. Heterogeneity analysis reveals a more pronounced increase for small enterprises located in regions with less stringent environmental regulations and in industries with higher degree of market concentration. Our conclusion suggests that the government should pay attention to the survival and development of small enterprises. This entails ensuring fair market competition on digital platforms and preventing the dominant enterprises from abusing their information advantages to jeopardize the interests of small enterprises.

Impact of digital technology innovation on carbon intensity: evidence from China's manufacturing A-share listed enterprises.

Current studies do not provide a consensus on whether digital technology innovation can reduce enterprise carbon intensity despite the rise of the digital economy. This paper examines the role and influence pathway of digital technology innovation on enterprise carbon intensity using data from A-share listed enterprises in China's manufacturing industry from 2012 to 2021. The findings indicate that (1) digital technology innovation has been found to significantly reduce enterprise carbon intensity, as confirmed by numerous robustness and endogeneity tests. However, its inhibitory effect on carbon intensity shows a marginal decreasing trend. (2) In the heterogeneity analysis, it was found that digital technology innovation significantly reduces the carbon intensity of consuming coal, coke, kerosene, and diesel. From various perspectives, including enterprise, industry, and external environment, there are significant differences in the carbon reduction effects of digital technology innovation. (3) The analysis of impact paths reveals that digital technology innovation can affect enterprise carbon intensity through three paths: improving productivity, enhancing green innovation efficiency, and adjusting energy consumption. (4) Upon further analysis, it was discovered that the spillover effect of digital technology innovation is more pronounced in the industry cohort of enterprises. Additionally, digital technology innovation plays a positive role in enhancing enterprise ESG performance. The paper's findings offer empirical evidence and decision-making references for the government to develop reasonable policies for reducing carbon emissions, promoting green and low-carbon enterprise transformation, and actively and steadily achieving the goal of carbon peaking and carbon neutrality.

Testing the EKC hypothesis for ecological and carbon intensity of well-being: The role of forest extent.

The G-20 countries represent a considerable percentage of the global economy and are crucial in matters to do with support for environmental sustainability. The uniqueness of this study lies in determining the effects of forests on human well-being and environmental degradation with respect to G20, offering a unique perspective regarding the efforts to battle climate change. The study analyzed the impact of income, forest extent and education on ecological and carbon intensity of well-being following the Environmental Kuznets Curve (EKC) hypothesis. Based on annual data from 1990 to 2022 and employing the Method of Moments Quantile Regression, the results validate the presence of an inverted U-shaped relationship between GDP and environmental well-being which refers to the existence of EKC. Our results connect improved ecological and carbon intensity of well-being with expanding forest extent and improving education levels. Forest management combined with educational management work as an effective mechanism reducing environmental degradation while also positively contributing to human well-being. In addition, through these informed and rational decisions, policy makers can promote the environmental stability of forests.

The Impact of Renewable Energy Development on Regional Carbon Emission Reduction: Based on the Spatio-Temporal Analysis of 30 Provinces in China.

The development of renewable energy has become an important means for the world to cope with climate change, ensure energy security, and protect the ecological environment. Using the panel data of 30 provinces in China from 2013 to 2021, this study used the mediating effect model and the spatial Durbin model (SDM) to explore the mechanism and spatial effects of renewable energy development on China's regional carbon emission reduction. The results show that: (1) Renewable energy development can help to reduce carbon emission intensity. (2) The results of mechanism analysis show that renewable energy development reduces carbon intensity by improving energy structure, promoting industrial structure optimization, and industrial structure upgrading. (3) The development of renewable energy can not only reduce the local carbon intensity but also have a positive spillover effect on the carbon intensity of neighboring regions. (4) Further analysis shows that the long-term effect of renewable energy development on carbon emissions is greater than the short-term effect. At the same time, the heterogeneity analysis shows that compared with the Yellow River basin, the development of renewable energy has a significant carbon emission reduction effect in the Yangtze River Economic Belt region. Energy-rich areas fall into the "resource curse", which makes the carbon emission reduction effect of renewable energy development not significant. This paper has certain reference significance for promoting reasonable decomposition between regions and formulating renewable energy development policies.

[Spatiotemporal Interaction Characteristics and Transition Mechanism of Carbon Intensity in China's Transportation Industry].

This research was conducted using many spatial analysis approaches to dissect the spatiotemporal interactive characteristics of carbon emission intensity within the transportation sector from 2002 to 2020. An in-depth exploration of their transition mechanisms was conducted by nesting the obtained timewarp types with the panel quantile model. Finally, the geodetector model aligned with different transition mechanisms was employed to investigate and analyze the interaction effects among various factors influencing carbon intensity in the transportation sector. The results indicated that:① The carbon emission intensity of the transportation sector in 30 provinces and regions of China showed an overall downward trend with fluctuations, and the spatial clustering level was relatively stable. ② The spatiotemporal interactive features of ESTDA revealed that the relationship between the northwest region and its adjacent spatial units was unstable, with significant variations and fluctuations. In contrast, economically developed areas such as coastal cities in the eastern part had established mature transportation networks, resulting in a relatively stable local spatial pattern, though a few areas still exhibited spatiotemporal competitiveness. ③ The spatiotemporal transition of carbon intensity in the transportation sector could be categorized into four driving or constraining modes(the population economy urbanization constraint model, population economy urbanization facility constraint model, technology consumption industry-driven model, and technology industry regulation-driven model). Most provinces were influenced by the low quantile constraint and high quantile drive modes, with only a few affected by the high quantile constraint and low quantile drive modes, the majority of which were located in the northwest or southwest regions. ④ Further, we introduced the geographical detector model based on the identified mechanism of carbon emission intensity transition in the transportation sector, emphasizing the coordinated development of multiple factors and strengthening inter-regional collaborative governance.

The role of financial innovation in carbon intensity reduction: perspectives from energy structure transition and fiscal policies.

Carbon emissions are important factors causing global warming, which requires global efforts to deal with. In this paper, we investigate the mechanism of financial innovation on reducing carbon emissions in China by constructing a financial innovation development index with factors of green finance as well as fintech development. Empirical results show that financial innovation contributes to reduce carbon intensity by promoting energy structure transition as well as public fiscal expenditure on energy conservation and environmental protection. Moreover, heterogeneity exists in the effect of financial innovation on carbon emission reduction. Financial innovation has a significant role in reducing carbon intensity in eastern regions, but has a relatively small influence on central and western regions. Furthermore, financial innovation has a lag effect on reducing carbon intensity.

Exploring the dual impact: Dissecting the impact of tourism agglomeration on low-carbon agriculture.

Despite extensive research on the relationship between tourism and agriculture, the specific impact of tourism on agriculture's low-carbon transition has not been thoroughly investigated. This study analyzes the effects of tourism agglomeration on agricultural carbon intensity across 30 Chinese provinces from 2001 to 2020. It is framed within the context of rural digitalization, with a particular emphasis on the integration of agro-tourism and the total factor productivity of agriculture. Utilizing spatial econometric models, we find that tourism agglomeration hinders the low-carbon transition in agriculture by influencing carbon intensity both directly and indirectly. At the national level, the impact of tourism agglomeration follows an inverted-U curve with respect to agro-tourism integration and carbon intensity. At the regional level, the effects vary, with weaker indirect influences in major grain-producing areas. Furthermore, rural digitalization appears to lessen the adverse impacts of tourism on carbon intensity. This study also identifies significant spatial spillover effects from tourism agglomeration. The findings suggest that provinces with high tourist influx should enhance investments in climate-smart agricultural practices and technologies to counteract these negative impacts. Moreover, integrated governance of tourism and agriculture is essential for achieving carbon neutrality in both sectors.

Carbon emissions changes of animal husbandry in China: Trends, attributions, and solutions: A spatial shift-share analysis.

China is a major livestock producer confronting the dual challenges of rising demand for animal-based food consumption and decreasing carbon emissions. To effectively address these issues, it is crucial to understand the trends of carbon emissions from animal husbandry and the competitive advantages of carbon emission reduction in different regions. This study uses panel data from 31 provinces from 2004 to 2020 to investigate the contributing factors to carbon emissions and explore ways to reduce carbon intensity in animal husbandry. The analysis employs spatial shift-share analysis and the spatial Durbin model. Our findings indicate that life-cycle carbon emissions associated with animal husbandry in China decreased from 572.411 Mt CO2eq to 520.413 Mt CO2eq over time, with an average annual decline of 0.568 %. The annual contribution of output value and internal industry-mix adjustment to carbon emission growth is 22.639 MT CO2eq and 6.226 MT CO2eq, respectively. On the other hand, the annual contribution of carbon efficiency improvement to carbon emission reduction is much higher, at 36.316 MT CO2eq. However, there is significant regional heterogeneity in the spatial decomposition of the carbon efficiency change component. The Northeastern region, Northwest and along the Great Wall demonstrate neighborhood advantages in enhancing carbon efficiency. In contrast, the South China and Southwest regions rely more on local carbon efficiency advantages to reduce the carbon intensity of animal husbandry. Furthermore, the carbon intensity in local and neighboring areas can be reduced through environmental regulations and industrial agglomeration. While technical progress significantly negatively impacts carbon intensity in neighboring regions, it does not contribute to reducing the carbon intensity of local animal husbandry. The findings provide valuable insights for local governments, aiding them in recognizing the pros and cons of carbon reduction in animal husbandry and strengthening regional cooperation in emission reduction management.

Impact of Environmental Protection Tax on carbon intensity in China.

In the context of increasingly severe global climate change, finding effective carbon emission reduction strategies has become key to mitigating climate change. Environmental Protection Tax (EPT), as a widely recognized method, effectively promotes climate change mitigation by encouraging emission reduction behaviors and promoting the application of clean technologies. Based on data from 282 cities in China, this paper takes the official implementation of the EPT in 2018 as the policy impact and the cities with increased tax rates for air taxable pollutants as the treatment group and uses DID model to systematically demonstrate the relationship between the implementation of the EPT and carbon intensity (CI) and further explores the possible pollutant emissions and green innovation mediating effects. The findings show that (1) the implementation of EPT can effectively reduce CI by about 4.75%, and this conclusion still holds after considering the robustness of variable selection bias, elimination of other normal effects, policy setting time bias, and self-selection bias. (2) The implementation of EPT can reduce CI by reducing pollutant emissions and improving the level of green innovation. (3) There is obvious regional heterogeneity in the carbon reduction effect of EPT, and the implementation of EPT has a more significant effect on CI in medium-tax areas, low environmental concern areas, general cities, and eastern regions. This paper not only provides a new analytical perspective for systematically understanding the carbon emission reduction effect of EPT but also provides policy insights for promoting regional green transformation and advancing carbon peak carbon neutralization.

Digitization and carbon emissions: how does the development of China's digital economy affect carbon intensity?

The digital economy and the pursuit of carbon peak and carbon neutrality have emerged as crucial focal points for China's future development. However, the intricate relationship between the digital economy and carbon intensity remains uncertain. Based on the construction of the digital economy evaluation index system, using panel data for 30 provinces in China from 2011 to 2019, this study estimates the impact of the digital economy development on carbon intensity by adopting the system-generalized method of moments (SYS-GMM) technique. The results show that the digital economy can effectively reduce the carbon intensity. This conclusion was supported by robustness tests. However, the carbon emission reduction effect of the digital economy exhibits heterogeneity with respect to the digital economy dimensions and regions. In addition to digital industrialization and industrial digitization reducing the carbon intensity, the digital economy development carrier has an inverted U-shaped nonlinear relationship with carbon intensity. Additionally, the digital economy has a more obvious inhibitory effect on carbon intensity in the eastern region. Most importantly, besides the mediating effects of technological progress and financial development, this paper finds that the digital economy can increase carbon intensity through human capital accumulation. These conclusions provide a certain scientific basis for the effective implementation of China's digital economy and carbon peak and carbon-neutral development strategy.

Paving the path towards carbon neutrality: revealing the contribution of the "Belt and Road" Initiative in mitigating carbon intensity.

This study explores the crucial contribution of the "Belt and Road" Initiative (BRI) in diminishing carbon intensity and facilitating progress towards carbon neutrality, addressing the pressing global issue of climate change. Given its status as the world's foremost carbon emitter, China encounters significant pressure to alleviate its emissions. Launched in 2013, the BRI emphasizes economic development along its route while highlighting environmental protection in the regions involved. Despite extensive analyses of the BRI's economic impact, its environmental consequences have received insufficient attention, hindering a comprehensive evaluation of the initiative and obstructing the constructing of an environmentally optimal road. Empirical findings reveal a substantial reduction in carbon emission intensity in provinces along the BRI route, with robustness tests (change the time window period and dynamic effect) validating result consistency. The BRI achieves this reduction by alleviating congestion, enhancing transportation infrastructure, fostering commuting agglomeration, optimizing energy utilization, and lowering carbon intensity. Further analysis uncovers a mediating chain effect, establishing a conduction mechanism of "BRI brings on transportation infrastructure effect and then leads to travel agglomeration effect and then to congestion improvement effect and then to energy utilization effect and then eventuates carbon intensity reduction." This study offers crucial insights for policymakers aiming to make informed decisions towards the green road construction of the BRI, contributing to global efforts to combat climate change.

Beyond the barrels: The impact of resource wealth on the energy-economy-climate targets in oil-rich economies.

This study models the Kaya identity equation for carbon dioxide (CO2) emissions in a panel of 20 oil-rich countries from 1994 to 2019. The estimators used are robust to cross-sectional dependence and allow for heterogeneous slope coefficients. The results indicate that natural resource extraction hinders environmental sustainability in oil-rich countries by altering the structural composition of their consumption mix towards energy- and carbon-intensive technologies. However, this relationship is only significant after reaching a turning point level of resource extraction. This suggests that the carbon curse is only triggered at higher levels of resource dependence, supporting a U-shaped relationship between natural resource extraction and CO2 emissions. The threshold for the natural rents to GDP ratio, beyond which natural resource extraction triggers the carbon curse, is found to be 12.18 %. The vulnerability assessment reveals that 17 countries in the panel, including Algeria, Kazakhstan, the United Arab Emirates, Iran, Iraq, Kuwait, Qatar, Oman, Saudi Arabia, the Congo Republic, and Libya, are already within the carbon curse zone. From a policy perspective, promoting sustainable development in oil-rich economies requires a shift towards renewable energy sources, reducing reliance on fossil fuels, and widespread adoption of energy efficiency and conservation mechanisms.

Carbon emissions of urban rail transit in Chinese cities: A comprehensive analysis.

Thoroughly exploring carbon emissions within Urban Rail Transit (URT) systems is crucial for effectively reducing emissions while satisfying increasing energy demands. This study evaluated the spatiotemporal characteristics of carbon emissions in China's URT sector. Tapio decoupling and the Logarithmic Mean Divisia Index, used to scrutinize decoupling states and identify principal contributing factors, respectively, revealed the following: (1) Total emissions increased by 217 %, with significant spatiotemporal heterogeneity from 2015 to 2022. Type I and Type II cities accounted for >85 % of emissions but exhibited lower carbon intensity. (2) Most URT cities showed expansion-negative decoupling between economic growth and carbon emissions. Developed regions show strong decoupling, and the overall decoupling status improved in 2021-2022. (3) Emissions growth was influenced by energy intensity and economic activity, and transportation intensity was the main inhibitor for Type I cities and a driving force for other cities. Finally, recommendations for carbon emission reduction in the URT industry are proposed.

Research on the path of building carbon peak in China based on LMDI decomposition and GA-BP model.

The building sector contributes significantly to carbon emissions, impeding China's progress toward its 2030 carbon emissions peak target due to the limited utilization of renewable energy sources. This study aims to forecast the peak and timing of carbon emissions in China's construction industry to chart a low-carbon roadmap for the sector's future. Initially, an extended logarithmic mean divisia index (LMDI) decomposition model, based on the Kaya identity, is proposed to gauge the contribution levels of driving factors affecting building carbon intensity. Subsequently, a hybrid prediction model (IGA-BP) is constructed, employing an optimized two-hidden-layer neural network via a genetic algorithm, to forecast building carbon emissions and intensity. Additionally, four scenarios are outlined, each defining pathways to simulate emissions peak, carbon peak timing, and intensity within the Chinese building sector from 2020 to 2050. The research findings reveal: (1) The final emission factor of buildings primarily drives the surge in building carbon intensity, while the industrial structure stands as the most significant limiting factor. (2) Compared to alternative models, the proposed hybrid prediction model more effectively captures the evolution pattern of carbon emissions. (3) The prediction results indicate that China's building carbon intensity has reached its peak. Pathway 12 closely aligns with the sector's carbon emissions peak, projecting a peak value of 5.609 billion tons in 2029. To attain this pathway, China needs to develop more precise and feasible emission reduction strategies for its buildings. Overall, the research outcomes furnish robust references for decision-making in future efforts aimed at reducing building emissions.

The impact of clean energy demonstration province policies on carbon intensity in Chinese counties based on the multi-phase PSM-DID method.

Based on China's empirical data from 2000 to 2020 of 1875 county-level administrative units, combined with the multi-phase by the propensity score matching and difference-in-difference (PSM-DID) model, this paper studies the impact of clean energy demonstration province policies on the carbon intensity of pilot counties, and its further impact on carbon emissions and economic development level. The results showed that 1. from a county-level perspective, although the economic development level of the pilot areas of clean energy demonstration provinces has improved as the carbon emissions have also increased, what is more, the carbon intensity has also significantly improved in this process; 2. there is no time lag in the impact of policies on the carbon intensity of counties, and the impact effects gradually increase over time along with strong regional heterogeneity; 3. the clean energy demonstration policy has weakened the technological level of the county and reduced the proportion of industrial-added value to GDP, thereby increasing the carbon intensity of the county through these intermediaries.

Effect of agricultural fiscal expenditures on agricultural carbon intensity in China.

Few studies provide direct evidences that agricultural fiscal affects agricultural carbon intensity. This study tries to fill this gap. Using panel data of 30 provinces in China from 2005 to 2019, we conclude that agricultural fiscal expenditures significantly reduce agricultural carbon intensity. The result is still robust after employing the provincial agricultural leaders' birthplace information as an instrumental variable. Further study shows that the negative effect of agricultural fiscal expenditures on agricultural carbon intensity is more pronounced in regions with less corruption and is also more visible in central, western, and inland regions than other areas. For this effect, agricultural technological improvement and structure optimization are possible channels, but not operation scale expansion. Interestingly, although agricultural fiscal expenditures reduce the local agricultural carbon intensity, neighbor regions' carbon intensities are increased due to fiscal rivalry.

A study on the two-level allocation of carbon emission quotas in China at the provincial level.

Carbon emission reduction is an essential means to achieve the "double carbon goal," and the scientific and reasonable allocation of carbon emission quotas (CEQ) is the basis for promoting carbon emission reduction. In this study, the first level was based on the entropy TOPSIS scores of provinces under the principles of fairness, efficiency, sustainability, and feasibility and used the K-mean clustering method to cluster the 30 provinces and allocate the CEQ to each zone group; the second level consolidated the impacts of the four principles and the marginal abatement costs of CO2 to allocate CEQ to the provinces within the zone group. Finally, each province's initial spatial balance of CEQ (ISBQ) is classified and evaluated. The study shows that the most quotas are for Guangdong, Zhejiang, and Inner Mongolia, and the least for Ningxia, Shanxi, and Guizhou. This study compares the results of CEQ allocation with the current carbon emission scale and finds that 11 provinces, such as Shandong and Hebei, show a deficit in future carbon emission space, and 19 provinces, such as Hainan and Beijing, show a surplus in carbon emission space. Given each province's different emission reduction tasks and pressures, differentiated emission control policies are the key to achieving China's "2030 target".