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Effectively tackling extreme climate change requires sound knowledge about carbon emissions and their driving forces. Currently, agricultural carbon emission assessment often deals with its inventory, efficiency, determinants, and response independently, which will leave out the complex interactions among its various components, thus there is a lack of comprehensive, scalable, comparable explanations for agricultural carbon emissions. Herein, we introduce an integrated agricultural carbon emission assessment framework (IEDR): Inventory (I) × Efficiency (E) × Determinants (D) × Response (R), which was then applied to an illustration for the county-level agricultural carbon emissions in Hunan Province, China. Results show that: (1) Agricultural carbon emission inventory (ACEI) increased from 20.06 × 10 6 tC in 2006 to 21.99 × 10 6 tC in 2014 and decreased to 19.07 × 10 6 tC by 2020, depicting a fluctuating trend. Meanwhile, there was remarkable spatial heterogeneity, with higher ACEI in the North and South than in the East and West. (2) Agricultural carbon emission efficiency (ACEE) increased from 0.8520 in 2006 to 0.8992 in 2020, depicting a growing trend driven by technological progress. Spatially distributed in contrast to ACEI, regions with higher ACEE were located in the eastern and western areas. (3) ACEI was negatively correlated with ACEE (-0.657), indicating that increasing ACEE is a key strategy for reducing emissions. (4) The natural environment, rural development level, and policy support had critical impacts on ACEE and ACEI. In particular, the cultivated area and rural water affairs development were significant influences on ACEE and ACEI. Given the externalities of carbon emissions and its important public goods characteristics of the atmosphere, local carbon issues are also global concerns. Therefore, the case study of the IEDR model not only validates this theoretical paradigm and realizes regional responsibility for global carbon reduction but also supports and expands the theoretical and empirical corpus in the field of agricultural carbon emissions and efficiency, providing insights and references for other global regions facing similar challenges., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Both market-oriented and government-led spatial agglomeration of factories profoundly affect China's carbon emission intensity. However, few studies have comprehensively examined the effect of spatial agglomeration of factories driven by different motivations on carbon emission intensity, and often ignore the moderating effect of local government attention to the environment. This study attempts to incorporate market-oriented and government-led spatial agglomeration of factories into the same analytical framework and examine their interaction effects on carbon emission intensity. We first calculate an Agglomeration Index based on the geographic coordinates of micro-factories to measure market-oriented agglomeration of factories, and take development zone policies as a proxy for government-led agglomeration of factories. Based on city-level data from 1998 to 2013, we empirically analyze their impacts on carbon emission intensity and further explore regional heterogeneity and mechanisms. The results show that both the market-oriented and government-led spatial agglomeration of factories can significantly reduce carbon emission intensity through scale effect, innovation effect and structure effect. However, the agglomeration of factories caused by government forces has weakened the original effect of market-oriented agglomeration of factories in reducing carbon emission intensity. Heterogeneity analysis indicates that government-led agglomeration of factories is more effective in reducing carbon emission intensity in less-developed regions. Furthermore, the improvement of the governments' attention to the environment can further exert the positive effect of spatial agglomeration of factories on carbon emission intensity. Our study deepens the understanding of the effects of spatial agglomeration of factories caused by different motivations, and provides a reference for China and other developing countries to formulate relevant policies for reducing carbon emission intensity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The Yangtze River Delta (YRD) region plays a crucial role in achieving China's carbon peaking goal. However, due to uncertainties surrounding future economic growth, energy consumption, energy structure, and population, the attainment of carbon peaking in this region remains uncertain. To address this issue, this study utilized the generalized Divisia index method to analyze the driving factors of carbon emissions, including economy, energy, investment, and population. Subsequently, Monte Carlo simulations were combined with scenario analysis to dynamically explore the peak path of regional heterogeneity in the YRD from 2022 to 2035 under uncertain conditions. The findings highlighted that economic uncertainty has the most significant impact on carbon emissions. Furthermore, reducing energy intensity and promoting the transformation of the energy consumption structure contribute to carbon reduction. The study also revealed that the carbon peak in the YRD exhibits regional heterogeneity. According to the baseline scenario, carbon emissions in the YRD will not peak before 2035. However, under the low-carbon development scenario, the carbon emissions of Zhejiang and Shanghai will peak before 2030. Moreover, under the enhanced emission reduction (EE) scenario, carbon emissions in Jiangsu, Zhejiang, and Shanghai will peak before 2025, while Anhui will reach its peak before 2030. Collectively, the entire YRD region is forecasted to attain a carbon emissions peak of 2.29 billion tons by 2025 under the EE scenario. This study provides valuable insights into the carbon emission trajectories of the YRD region under uncertain conditions. The findings can be instrumental in formulating carbon peaking policies that account for regional heterogeneity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Forests, the ancient wooden giants, are both symbols of natural beauty and reservoirs of carbon stocks. The current climate crisis has created an urgent need for an in-depth study of forest ecosystems and carbon stocks. Based on forest inventory data from field surveys and four bioclimatic zones [Zone 1 (Z1, humid forest), Zone 2 (Z2, semi-humid forest), Zone 3 (Z3, semi-humid to semi-arid forest-grassland), and Zone 4 (Z4, semi-arid typical grassland)], two methods [Method 1 (M1) and Method 2 (M2)] were used to estimate carbon stocks in forest ecosystems in Shaanxi Province, China, and explored the spatial patterns of carbon pools and potential influences. The total forest ecosystem carbon pool amounted to 520.80 Tg C, of which 53.60% was stored aboveground, 17.16% belowground, and 29.24% in soil (depth of 0-10 cm). Spatially, there were marked north-south gradients in both biomass (Z2 > Z3 > Z1 > Z4) and soil organic carbon densities (Z1 > Z2 > Z3 > Z4). The differences between aboveground and belowground biomass carbon density across broadleaf, needle-leaf, and broadleaf and needle-leaf mixed forest were not pronounced, while soil organic carbon density had the order of broadleaf (18.38 Mg C/ha) > needle-leaf (11.29 Mg C/ha) > broadleaf and needle-leaf mixed forest (10.33 Mg C/ha). Under an ideal scenario that excludes external factors, mainly forest growth, the sequestration potential of forest biomass by 2032 was estimated by M1 as 85.43 Tg, and by M2 to be substantially higher at 176.21 Tg. As of 2062, M1 estimated 155.97 Tg of sequestration potential for forest biomass. The spatial patterns of forest biomass and soil carbon density were closely related to climatic factors, and these relationships allowed the spatial division into two distinct climatic regions. Moreover, biomass carbon density was significantly correlated with the normalized difference vegetation index, soil silt, and elevation. This study provides key information for promoting the strategic shift from light-green to deep-green forest systems in Shannxi Province and updates the estimation methods of forest ecosystems' carbon pools based on field surveys., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Building a low-carbon economy can help cities effectively mitigate climate risks, but it is challenging for developing countries. Using a difference-in-difference and event study model, we investigate the joint impact of China's Low-Carbon City Pilot (LCCP) on carbon emissions and economic performance. Our findings show that the LCCP significantly reduces carbon emissions and increases gross revenues, employee count, and fixed assets without compromising the net profit of manufacturing firms. The LCCP has a cumulative effect, with the positive joint impact increasing gradually over time. A heterogeneity analysis shows that the later pilot cities have not achieved better carbon emissions and economic performance than the early pilot cities. The reason for the positive joint effect of LCCP is that the Porter effect outweighs the cost effect. These findings contribute to knowledge about how developing countries can develop a low-carbon economy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The carbon generalized system of preferences (CGSP) is an innovative incentive mechanism implemented by the Chinese government, which has also become an important part of carbon emission reduction at the living end, and it is of great significance to study whether the Pilot Policy can reduce the carbon emissions of residents. This study firstly accounts for the total carbon emissions and per capita carbon emissions of the residents of 284 cities in China, and on this basis, adopts the SCM method to quantitatively study and analyze the overall and local implementation effects of CGSP in China by taking the first batch of CGSP pilots in China as an example, and further applies the mediation effect model to test the pathways of the role of CGSP. The main findings of the study are as follows: (1) During the period of 2010-2020, the total carbon emissions from urban residents' living in China showed a yearly growth trend, from 36,623.98 ×10 -2 Mt in 2010-85,241.20 ×10 -2 Mt in 2020, an increase of 8.83%. Total carbon emissions present a structural difference of "electricity consumption > central heating > private transport > gas (oil, natural gas) consumption". (2) Overall, the implementation of the CGSP had a robust positive impact on the overall carbon emission reduction in the pilot cities, with an average annual emission reduction effect value of 36.53 ×10 -2 Mt. Locally, the annual net policy effect values of Dongguan, Zhongshan, Heyuan, and Guangzhou are 6169.79 ×10 -2 , 26,600.17 ×10 -2 , 17,081.34 ×10 -2 and 9393.36 ×10 -2 Mt respectively. (3) CGSP has a good carbon emission reduction effect by suppressing the impact on residents' carbon emissions through enhancing the city's innovation capacity and promoting electricity saving and consumption reduction, while the mediating effect played by the promotion of green and low-carbon travel in the pilot policy is not significant. Finally, based on the research findings, relevant suggestions are targeted., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Although our knowledge of national carbon emission trading system and green certificate trading system are powerful incentive instruments that can deliver on increasingly ambitious climate targets in China, there remains an uncertainty of systems' structural reforms. This study builds on and extends a well-established dynamic computable general equilibrium (CGE) model to incorporate carbon trading system and green certificate trading system into the modeling framework, simulating a diverse of system development pathways further allows an exploration of the many possible policy effect. Then, using total factor productivity as a comprehensive indicator to asses policy effectiveness, the evolutionary trend of comprehensive effects under different paths are separately evaluated to discover the reforms' optimal range. Our work offers main results: First, these instruments provide a price signal. The introduction of a carbon allowance auction drive up carbon prices, while the implementation of a green certificate punishment and the expansion of the trading scope promote an increase in green certificate prices. Second, all policy scenarios that help reduce carbon emission intensity and optimize the power supply structure. However, in achieving the net-zero goal, the green certificate policy incurs more economic costs than the carbon trading policy. Indeed, the combination of multiple policy tools alleviates the decline of social welfare levels. Third, synergism design among policy tools: the focus should be on carbon trading policy from 2021 to 2030, green certificate trading policy from 2030 to 2050, and strengthened policy from 2050 to 2060. Reform measures within policies may need to be introduced in a timely manner. This study offers specific insights and tailored policy proposals to support policymakers in balancing environmental goals with economic and social needs in light of the aforementioned findings., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

China's double carbon target aims to improve human well-being and sustainable development. Energy transformation welfare performance (ETWP) is the efficiency of energy transition (ET) in enhancing human well-being. ETWP considers both human well-being and sustainable development. Research on its driving force is helpful in achieving the double carbon goal. Thus, this paper used Logarithmic Mean Divisia Index Model, Fixed Panel Regression Model and Grey Relational Analysis Model to analyze China's ETWP from 2006 to 2022 and predicted ETWP of 31 provinces from 2023 to 2030. The results showed that: (1) ETWP had two rising periods in 2006-2014 and 2015-2022. (2) The government's rationalization policy on energy and environment and technological innovation ability were fundamental driving forces for improving ETWP. (3) There were obvious spatial and temporal distinctions in ETWP, and it would bring out different degrees in most areas. Thus, ET should be promoted by improving the ecological environment and resource utilization efficiency; The importance of the role of scientific and technological innovation and policies should be focused on in promoting ETWP; ET policies based on local developments should be formulated and the energy structure should be changed., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The stabilization of growth and preservation of employment are the primary objectives of the current new economic normal. Investigating whether the low-carbon transition can be an opportunity or a shock for employment expansion in green development requires thorough examination. This study utilizes multi-temporal difference-in-difference (DID) models, analyzing comprehensive panel data from China (2007-2019) to assess the impact of the Low-carbon City Pilot (LCCP) policy on employment at meso-regional and micro-firm levels. Empirical findings robustly reveal that LCCP significantly boosts employment, with average treatment effects of approximately 0.548% and 5.892% at regional and firm scales, respectively. Positive impacts vary based on ownership, location, industry type, and energy consumption within enterprises. Notably, state-owned enterprises, those in the eastern region, engaged in secondary industries, and with high energy consumption experience pronounced positive effects. Mechanism analysis further reveals that LCCP boosts employment via promoting government environmental subsidies and expanding enterprise investment scale to create more jobs. These findings provide policy recommendations for further promoting low-carbon transition and expanding employment to achieve the win-win goal of sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The trading of carbon emissions is a crucial regulatory method to address environmental pollution issues. This study takes China's carbon emission trading pilot policy established in 2013 as a quasi-natural experiment and uses the DID model to empirically test the urban panel data from 2006 to 2019. The results show that the carbon emission trading pilot policy can effectively reduce urban environmental pollution, and this effect is more noticeable in mid-western cities, northern cities, cities with fewer resources, and large-scale cities. In addition, to address the urban environmental pollution problem through this policy, the government is encouraged to raise its environmental protection awareness and put more effort into the innovation of technology. In general, this study uses carbon emission trading policies from China to confirm that market-based incentive environmental regulation tools can effectively reduce environmental pollution in urban areas. These findings can provide more theoretical support and empirical evidence for the government to use mechanisms of the market to effectively solve pollution problems, improve ecological environment quality, and accelerate the realization of green economy., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Research Question: In the context of global low-carbon emission reduction, how to achieve green and high-quality development has become a major issue for the Communist Party of China (CPC) and the Chinese government recently. Based on the data of China's listed companies from 2013 to 2020, this paper uses Python to implement text analysis of annual reports, and explores the relationship between political acuity and carbon-efficiency synergies (CES) from the perspective of enterprise initiative., Research Findings: We found that (1) political acuity positively affects carbon-efficiency synergies. (2) Increased political acuity can reduce carbon emissions, but the effect on economic efficiency is not obvious. That is, low carbon takes the lead in raising the level of carbon-efficiency synergies. (3) Environmental regulations can positively regulate the relationship between political acuity and carbon-efficiency synergies. (4) Political acuity in southern China, carbon neutral and non-state-owned enterprises (NSOEs) will have a more pronounced effect on carbon-efficiency synergies., Academic Implications: From the perspective of the root causes of political linkages, we find the synergies between formal and informal institutions, and the key factors for policy implementation., Policy Implications: This paper is helpful for enterprises to improve the synergies of emission reduction and efficiency promotion, and has practical implications for the government to promote green and high-quality development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The digital economy (DIE), a new economic form with digitalization at its core, has become an important driving force for promoting regional economy development. In the context of the COVID-19 pandemic, exploring the impact path of the DIE on carbon emission efficiency (CEE) is conducive to giving full play to the "carbon-reduction-and-efficiency-enhancement" role of the DIE, and to promoting the realization the "dual carbon" goal of carbon peak and carbon neutrality. In this paper, the Yellow River Basin (YRB) and the Yangtze River Economic Belt (YREB) are taken as study areas, the panel Tobit model is used to explore the impact of the DIE on CEE, and the intermediary-effect model and threshold-effect model are constructed to test the intermediary and threshold effects of technological innovation, respectively. The results show that the DIE has a U-shaped nonlinear impact on CEE in both the YRB and the YREB and that the impact has regional heterogeneity. Technological innovation can play a mediating effect between the DIE and CEE, whereas the mediating effect in the YRB is stronger than that in the YREB. Technological innovation has a threshold effect on the DIE to improve CEE, while the threshold value in the YREB is higher than that in the YRB. Furthermore, this paper proposes some suggestions to guide regional low-carbon and sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

China's commitment to attaining carbon neutrality by 2060 has galvanized research into carbon sequestration, a critical approach for mitigating climate change. Despite the rapid urbanization observed since the turn of the millennium, a comprehensive analysis of how urbanization influences urban carbon storage throughout China remains elusive. Our investigation delves into the nuanced effects of urbanization on carbon storage, dissecting both the direct and indirect influences by considering urban-suburban gradients and varying degrees of urban intensity. We particularly scrutinize the roles of climatic and anthropogenic factors in mediating the indirect effects of urbanization on carbon storage. Our findings reveal that urbanization in China has precipitated a direct reduction in carbon storage by approximately 13.89 Tg of carbon (Tg C). Remarkably, urban sprawl has led to a diminution of vegetation carbon storage by 8.65 Tg C and a decrease in soil carbon storage by 5.24 Tg C, the latter resulting from the sequestration of impervious surfaces and the elimination of organic matter inputs following vegetation removal. Meanwhile, carbon storage in urban greenspaces has exhibited an increase of 6.90 Tg C and offsetting 49.70% of the carbon loss induced by direct urbanization effects. However, the indirect effects of urbanization predominantly diminish carbon storage in urban greenspaces by an average of 5.40%. The degree of urban vegetation management emerges as a pivotal factor influencing the indirect effects of urbanization on carbon storage. To bolster urban carbon storage, curbing urban sprawl and augmenting urban green spaces are imperative strategies. Insights from this study are instrumental in steering sustainable urban planning and advancing towards the goal of carbon neutrality., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Microorganisms are essential components of underground life systems and drive elemental cycling between plants and soil. Yet, in the ecologically fragile Loess Plateau, scant attention has been paid to the response of microbial communities to organic carbon (C) chemistry of both leaves and soils under different revegetation conditions, as well as subsequent alternation in their C metabolic functions. Here, Fourier transform infrared (FTIR) spectrum, amplicon sequencing of 16S rRNA and ITS, and temporal incubation with Biolog-Eco 96 plates were combined to explore the vegetative heterogeneity of microbial community properties and metabolic functions, as well as their regulatory mechanisms in three typical revegetation types including Robinia pseudoacacia L. (RF), Caragana korshinskii KOM. (SL), and abandoned grassland (AG). We observed higher bacterial-to-fungal ratios (B: F = 270.18) and richer copiotrophic bacteria (Proteobacteria = 33.08%) in RF soil than those in AG soil, suggesting that microbes were dominated by r-strategists in soil under RF treatment, which is mainly related to long-term priming of highly bioavailable leaf C (higher proportion of aromatic and hydrophilic functional groups and lower hydrophobicity). Conversely, microbial taxa in AG soil, which was characterized by higher leaf organic C hydrophobicity (1.39), were dominated by relatively more abundant fungi (lower B: F ratio = 149.49) and oligotrophic bacteria (Actinobacteria = 29.30%). The co-occurrence network analysis showed that microbial interactive associations in RF and AG soil were more complex and connective than in SL soil. Furthermore, Biolog-Eco plate experiments revealed that microorganisms tended to utilize labile C compounds (carbohydrates and amino acids) in RF soil and resistant C compounds (polymers) in AG soil, which were consistent with the substrate adaptation strategies of predominant microbial trophic groups in different revegetation environments. Meanwhile, we observed greater microbial metabolic activity and diversity advantages in RF vegetation. Collectively, we suggest that besides the nutrient variables in the leaf-soil system, the long-term regulation of the microbial community by the C chemistry of the leaf sequentially alters the microbial metabolic profiles in a domino-like manner. RF afforestation is more conducive to restoring soil microbial fertility (including microbial abundance, diversity, interactive association, and metabolic capacity). Our study potentially paves the way for achieving well-managed soil health and accurate prediction of C pool dynamics in areas undergoing ecological restoration of the Loess Plateau., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Soil aggregates play pivotal roles in soil organic carbon (SOC) preservation and climate change. Biochar has been widely applied in agricultural ecosystems to improve soil physicochemical properties. However, the underlying mechanisms of SOC sequestration by soil aggregation with biochar addition are not well understood at a large scale. Here, we conducted a meta-analysis of 2335 pairwise data from 45 studies to explore how soil aggregation sequestrated SOC after biochar addition in agricultural ecosystems of China. Biochar addition markedly enhanced the proportions of macro-aggregates and aggregate stability, and the production of organic binding agents positively facilitated the formation of macro-aggregates and aggregate stability. Soil aggregate-associated organic carbon (OC) indicated a significantly increasement by biochar addition, which was attributed to direct and indirect inputs of OC from biochar and organic residues, respectively. Biochar stimulated SOC sequestration dominantly contributed by macro-aggregates, and it could be interpreted by a greater improvement in proportions and OC protection of macro-aggregates. Furthermore, the SOC sequestration of soil aggregation with biochar addition was regulated by climate conditions (mean annual temperature and precipitation), biochar attributes (biochar C/N ratio and pH), experimental practices (biochar addition level and duration), and agronomic managements (land type, cropping intensity, fertilization condition, and crop type). Collectively, our synthetic analysis emphasized that biochar promoted the SOC sequestration by improving soil aggregation in agricultural ecosystems of China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

From the perspective of innovation motivation, low-carbon innovation is divided into substantive low-carbon innovation and strategic low-carbon innovation. On this basis, this study empirically investigates the impact and mechanism of digital transformation on enterprise low-carbon innovation using data from China's A-share listed enterprises from 2005 to 2019. The results show that digital transformation promotes the overall level of enterprise low-carbon innovation, substantial low-carbon innovation, and strategic low-carbon innovation, with a more significant impact on strategic low-carbon innovation. For enterprises with large-scale, belonging to high-monopoly industries and located in high-marketization regions, the promotion of digital transformation is more significant and facilitates strategic low-carbon innovation more than substantive low-carbon innovation. Based on the internal capability perspective, enhancing management capability, R&D capability, and environmental information disclosure capability are three important channels of digital transformation affecting low-carbon innovation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In the era of climate change and carbon neutrality, China is actively coping with its carbon lock-in dilemma. In this context, the development of the digital economy is considered a possible path to carbon unlocking. This study contributes to the literature by providing a comprehensive analysis of how the digital economy can be leveraged to address carbon lock-in, highlighting the importance of formal environmental regulation and informal environmental regulation in enhancing this effect. Accordingly, following findings are highlighted. (1) Our primary findings provide strong evidence on the negative effect of the digital economy on carbon lock-in; by implication, improving the digital economy is an efficient measure for eradicating carbon lock-in. (2) The digital economy shows the greatest marginal impact on industrial lock-in, while its inhibiting effect on behavior lock-in is the least pronounced. Moreover, the digital economy plays a prominent role in curbing carbon lock-in in provinces with a higher level of physical, human, and social capital. And the asymmetric impacts of the digital economy on carbon lock-in are significant at most quantiles. (3) Environmental regulation is a significant moderator. Put differently, the synergy of formal environmental regulation and the digital economy, as well as informal environmental regulation and the digital economy, are important means to break carbon lock-in. (4) The carbon lock-in mitigation effect of the digital economy is caused mainly by increasing technical market turnover and the efficiency of energy consumption., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Urban areas contribute 85% of China's CO 2 emissions. Green finance is an important means to support green energy development and achieve the low-carbon transformation of high-energy-consuming industries. The motivation of this article is to investigate the impact and mechanism of green finance on urban carbon intensity. Most existing literature uses linear models to investigate urban carbon intensity, ignoring the nonlinear relationships between economic variables. The nonparametric models can fill the inherent shortcomings of linear models and effectively simulate the nonlinear nexus between economic variables. Based on the 2011-2021 panel data of 237 cities in China, this paper applies the nonparametric additive model to survey the influence of green finance on urban carbon intensity. Empirical findings exhibit that green finance exerts an inverted U-shaped effect on urban carbon intensity, indicating that the carbon reduction effect of green finance has gradually shifted from inconspicuous in the early stages to prominent in the later stages. Then, from the perspectives of region, city size, and carbon intensity, this article conducts heterogeneity analysis. The results show that the impact of green finance on various carbon intensities all exhibits obvious nonlinear feature. Furthermore, this article employs a mediation effect model to conduct mechanism analysis. The results display that technological progress and industrial structure are two important mediating variables, both of which produce an inverted U-shaped nonlinear impact on urban carbon intensity., Competing Interests: Declaration of competing interest We hope this article will be considered for publication in “Journal of Environmental Management”. No conflict of interest exits in the submission of this manuscript, and manuscript is approved by all authors for publication. I would like to declare on behalf of my co-authors that the work described was original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part. All the authors listed have approved the manuscript that is enclosed., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study explores the comprehensive effects of green finance (GF) on the low-carbon transition of the energy system (LTES) by analyzing panel data from 281 cities in China from 2006 to 2021. It is found that GF significantly reduces overall energy consumption and exhibits a U-shaped association with energy efficiency, while its relationship with the energy consumption structure is inverted U-shaped. After accounting for endogeneity in the robustness tests, these findings remain consistent and are therefore deemed reliable. A mechanistic analysis reveals that GF promotes industrial upgrading, technological progress, and economic agglomeration, collectively facilitating the LTES. The impact of GF on LTES shows considerable variation among regions, influenced by their levels of economic growth, extents of marketization, and governmental environmental preferences. Our findings provide new evidence for the relationship between GF and LTES, offering a scientific basis for formulating GF policies to accelerate this transformation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

China and the USA, as preeminent contributors to global carbon emissions, demonstrate discernible differentials in both magnitude and trajectories of their respective carbon outputs. This article employed two methods, Structural Decomposition Analysis (SDA) and Quantitative Structural Modeling, to scrutinize the underpinnings of these disparities through the lens of the global value chain. Drawing upon data from the World Input-Output Database (WIOD), our analysis revealed that the compounded influences of output composition, input intensity, input composition and input origin collectively elevated China's aggregate carbon footprint from 2000 to 2014, while the scale effect made China's carbon emissions lower than of the USA. Notably, China's carbon emissions surpassed those of USA, with the gap accentuating over time. The quantitative results of the structural model showed that the difference in carbon emissions between China and USA predominantly stem from disparities in productivity, production technology, factor intensity, factor endowment and direct carbon intensity. Differences in trade costs exhibited some discernible impact, their influence remains relatively marginal, whereas distinctions in consumption behaviors and trade imbalances minimally contribute to the observed differentials. These findings have important policy implications for global carbon reduction efforts and China's trajectory towards a low-carbon economic paradigm., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Digital technology advancement provides a significant impetus to achieve China's "dual-carbon" goals, yet it also gives rise to a series of challenges. Therefore, studying the relationship between digital technology innovation and carbon emission efficiency is of paramount importance. This study theoretically analyzes and empirically tests the influence of digital technology innovation (DTI) on total factor carbon emission efficiency (TFCE) using panel data from 268 Chinese cities between 2006 and 2021. The results indicate that: (1) DTI exhibits a "U-shaped" pattern on urban TFCE, with a decrease followed by an increase. (2) Conventional technological innovation (TI) also displays a "U-shaped" relationship with TFCE, with the turning point occurring earlier than that of DTI. DTI surpasses TI in bringing about later-stage improvements in carbon emission efficiency. (3) Mechanism tests reveal that digital technology innovation indirectly affects TFCE through energy effects, technological effects, structural effects, and regulatory effects. (4) The impact of DTI on urban TFCE varies significantly due to differences in geographical location and resource endowments. (5) The development of urban polycentricity advances the turning point at which DTI enhances TFCE while amplifying both the initial "pro-carbon" effect and the subsequent "carbon reduction" effect of DTI. (6) DTI has a spatial spillover effect on urban TFCE. This study provides empirical evidence and policy recommendations for policymakers to advance the digitalization, greening, and decarbonization transformation of cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Exploring the ecological utility of cultivated land's carbon metabolism offers policy insights for ensuring its healthy operation and promote the dual carbon goals (carbon peak and carbon neutrality). We employed ecological network analysis (ENA) and kernel density estimation to conduct an empirical study, taking Hubei Province from 2000 to 2020 as an example. The results revealed apparent negative effects of carbon metabolic flow on regional carbon balance. Specifically, cultivated land conversion into transportation and industrial land contributed significantly to the harmful carbon flow. Ecological relationships showed fierce competition for carbon storage, leading to overall adverse ecological effects. The ecological utility indicated detrimental impacts on the orderly functioning of land-use carbon metabolism. Cultivated land's carbon metabolism will be essential in achieving land-use carbon neutrality. Therefore, territorial spatial low-carbon optimization should be implemented to realize its green and sustainable development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Profound worldwide fleet electrification is thought to be the primary route for achieving the target of carbon neutrality. However, when and how electrification can help mitigate environmental impacts and carbon emissions in the transport sector remains unclear. Herein, the overall life-cycle environmental impacts and carbon saving range of two typical A-class vehicles in China, including electric vehicle (EV) and internal combustion engine vehicle (ICEV), were quantified by the life cycle assessment model for endpoint damage with localization parameters. The results showed that the EV outperformed the ICEV for the total environment impact after a travel distance of 39,153 km and for carbon emissions after 32,292 km. The ICEV was more carbon-friendly only when the driving distance was less than 3229 km/a. Considering a full lifespan travel distance of 150,000 km, the whole life-cycle average environmental impacts of EV and ICEV were calculated as 8.6 and 17.5 mPt/km, respectively, but the EV had 2.3 times higher impacts than the ICEV in the production phase. In addition, the EV unit carbon emission was 140 g/km, 46.8% lower than that of the ICEV. Finally, three potential reduction scenarios were considered: cleaner power mix, energy efficiency improvement and composite scenario. These scenarios contributed 19.1%, 13.0% and 32.1% reductions, respectively. However, achieving carbon peak and neutrality goals in China remains a great challenge unless fossil fuels are replaced by renewable energy. The research can provide scientific reference for the method and practice of emission reduction link identification, eco-driving choice and emission reduction path formulation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Environmental protection is a shared task among nations. In pursuit of its commitment to achieve carbon neutrality by 2060, China has implemented more robust energy-saving targets. This study utilizes panel data from 288 Chinese cities spanning from 2006 to 2020 to examine the policy effects of energy-saving targets on carbon neutrality. The findings reveal that (1) energy-saving targets positively impact carbon substitution, resulting in reduced carbon emissions and facilitating the progress towards carbon neutrality through three primary channels: energy governance, energy production, and energy consumption. (2) The influence of energy-saving targets on carbon neutrality exhibits a significant spatial spillover effect, driven primarily by the reduction in carbon emissions, although the spatial spillover effect of carbon substitution is relatively limited. The collaboration between the government and enterprises plays a crucial role in achieving carbon neutrality, while the engagement of the general public is yet to be fully realized. (3) However, the inadequacy of enhancing carbon neutrality through energy-saving targets lies in the compulsory emissions reduction behavior at the expense of sacrificing some economic benefits in cities that overachieve energy-saving targets. This undermines the coordinated development of ecology and economy. Therefore, it is recommended to establish a policy implementation monitoring system to ensure the scientific basis of policy objectives, enhance the level of green technology innovation, accelerate the digital transformation of enterprises, and establish a synergistic mechanism that involves multiple stakeholders., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This paper interprets the implicit carbon flows in global industrial sectors from a network perspective. Using the SNA-IO integrated model, along with cross-border input-output data from Eora26 (2000-2020) and global energy balance data, the implicit carbon emissions of global industrial sectors and their evolution are analyzed. A carbon emission network structure from an industrial chain perspective is proposed. The results indicate that the carbon emissions responsibility of an industry is not only associated with its own energy consumption. It also involves the carbon emissions transfer resulting from the exchange of products and services between upstream and downstream industries. Block model analysis reveals the carbon emission transfer relationships and their interconnections among global industrial sectors, tending towards an industry clustering pattern where "production side" converges with "demand side" coexisting in supply and demand. There are noticeable inequalities in wealth gains and environmental burdens between these blocks. This paper can provide targeted carbon reduction policy recommendations for various industrial sectors to participate in global responsibility allocation and promote the formation of a low-carbon global industrial sector network., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Stomatal conductance (g s ) and compensatory water uptake (CWU) are crucial processes in land surface models, as they directly influence the exchange of carbon and water fluxes between terrestrial ecosystems and the atmosphere. In this study, we integrated a new stomatal scheme derived from optimal stomatal theory (Medlyn's g s model), and an empirical CWU scheme into the Common Land Model (CoLM). Assessing the impacts on modeling gross primary productivity (GPP) and latent flux (LE) through observations obtained from eddy covariance (EC) measurements at three forest sites in China. Our results show that replacing the Ball-Berry's g s model (termed BB) with Medlyn's g s model (termed MED) did not bring about significant changes (had neutral impacts) in the performance of CoLM simulations at three forest sites. Considering the climate factors of annual mean precipitation to optimize key fitting parameters in g s exhibited improvement in model simulations. The average coefficient of determination (R 2 ) achieved to 0.65 for GPP and LE at three sites, and the normalized root mean squared error (NRMSE) decreased from 0.83 to 0.77 at those sites. Besides, incorporating CWU into the model improved its performance. The R 2 increased to 0.84 and RMSE decreased to 4.84 μmol m -2 s -1 for GPP, and the R 2 increased to 0.62 and RMSE decreased to 55.64 W m -2 for LE. Therefore, modifying the model process of both contributed more to enhancing the model simulations than relying solely on one of these functions. Our study highlights that the response of plant functional types (PFTs) to water stress can be effectively represented in g s models when coupled with biochemical capacity to quantify carbon and water fluxes in forest ecosystems or other ecosystems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Cities are one of the main sources of regional carbon emissions, and reducing urban carbon emission is the key to reducing emissions. The digital economy has transformed the economic operation mode, and it is a significant approach to support the "dual carbon goals" (carbon peaking and carbon neutrality). This article considers the externalities of the digital economy and carbon emissions. And we use spatial econometric models to analyze the effectiveness of digital economy in empowering carbon emissions reduction. Besides, we explore the static and dynamic spillover effects, and use spatial Durbin panel quantile model to analyze the digital economy's heterogeneity on carbon emissions. Research has shown that the digital economy has a remarkable carbon reduction effect, and the conclusion remains valid after considering robustness tests such as replacing the weight matrices, calculation methods, and proxy variables. The analysis of static and dynamic spillover effects indicates that the degree of the digital economy's impact on carbon emissions are significantly different. Heterogeneity analysis shows that as the digital economy develops from a low level to a high level, its impact on carbon emissions also shifts from positive promotion to negative suppression. This paper proposes a policy reference to help the development of digital economy and promote carbon neutrality in the face of severe environmental challenges., Competing Interests: Declaration of competing interest The authors affirm that they do not have any known competing financial interests or personal relationships that could have influenced the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Traditional manufacturing industry is in the early stages of transition to low-carbon innovative production, and is in urgent need of a low-carbon innovation system to achieve the goal of carbon neutrality. In order to realize the effective supervision of enterprise carbon emissions, this paper constructs a tripartite evolutionary game model among the corporate, government and public from the perspective of dynamic subsidies and taxes. The main results are as follows. First, the increase in government subsidies to a certain extent will help encourage companies to choose low-carbon innovative production strategies, but more subsidies are not always better. Excessive subsidies will increase the cost of government regulation and reduce the probability of government regulation. Second, the tripartite evolutionary game system does not converge under the static subsidies and taxes mechanism. But the system could quickly converges to the stable condition under dynamic subsidies and taxes. The stable point is the situation of corporate low-carbon innovation, government regulation, and public supervision. Third, the public intervention and supervision can effectively prevent the phenomenon of government misconduct and enterprises over-emission production. And the influence of public reward and punishment is more effective for the government than for enterprises., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study examines the effects of China's carbon trading policy on firm emissions and explores its impact mechanisms through financial and physical asset investments. The empirical analysis utilizes a fuzzy regression discontinuity design based on a sample of 427 industrial firms in China between 2014 and 2019. The results indicate that China's carbon trading policy incentivized firms to increase their financial investments while simultaneously discouraging physical capital investments. These shifts in investment patterns helped firms achieve their emission reduction targets. The study reveals that carbon trading policy in China has contributed to the financialization of firms, resulting in the erosion of firm assets and a decline in their overall competitiveness. Based on these findings, some policy recommendations are put forward., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In the context of global digitalization, fostering the expansion of the digital economy holds immense importance in promoting energy efficiency and reducing emissions. Utilizing a sample of 281 prefecture-level cities in China from 2003 to 2019, this research analyzes the impact of the digital economy on urban carbon emissions in China. This study employs various methods, including principal component analysis, fixed-impact model, and mediating effect model. Based on the research findings, the development of the digital economy has the potential to significantly reduce carbon emissions in metropolitan areas. Moreover, these effects are especially noticeable in cities located to the east of the Hu Huanyong Line. These cities are characterized by limited reliance on resources and a high level of marketization. Further research reveals that promoting technical innovation and modernizing industrial structures can reduce the intensity of carbon emissions in metropolitan areas. This study provides empirical evidence supporting the effective reduction of carbon emissions in developing countries during the evolution of the digital economy. It acts as a crucial cornerstone for guiding policies and executing strategies aimed at fostering top-tier economic development in the future. The findings of this study confirm the inhibitory effect of the digital economy on urban carbon emission intensity. However, this study has limitations in data samples, research scope, and depth of mechanism analysis, which prevent a full exploration of the spatial spillover effect of the digital economy and other factors. Therefore, the conclusions drawn in this study can only provide empirical evidence for identifying the relationship between the digital economy and carbon emission intensity to a certain extent. Future research should aim to expand on these aspects., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Soil aggregates are essential functional units involved in soil carbon sequestration, particularly in saline-sodic soils prone to severe carbon loss. In the present study, the distribution of aggregate-associated carbon fractions and their influencing factors were investigated after artificial utilization of saline soil in the Songnen Plain, Northeast China. Physicochemical properties, enzymatic activities, and bacterial communities were measured in various hierarchical aggregates among two natural land-use types (saline wasteland and degraded grassland) and three anthropogenic land-use types (artificial forest, upland field, and paddy field). The results indicated that, compared to saline wasteland, anthropogenic land use was witnessed an increase in macroaggregate proportions, and PF in large macroaggregates increased the most, while UF and FL were mainly increased in small macroaggregates. After transforming from natural land to anthropogenic land, the aggregate-associated carbon fractions (total organic carbon, readily soluble organic carbon, dissolved organic carbon, and microbial biomass carbon) increased, especially in small macroaggregates. All enzyme activities increased after artificial utilization, hydrolase (urease, amylase, and invertase), catalase, and β-glucosidase activities were highest in the small macroaggregates. Bacterial biomass was increased in all three aggregate types compared to natural land. Due to the influence of various factors on soil carbon storage, through partial least squares path modeling revealed that large macroaggregates were conducive to carbon storage. These findings suggested that artificial utilization of saline soil can increase large macroaggregate proportions and the abundance of aggregate-associated carbon, resulting in increased soil carbon stocks, with PF having the greatest carbon sequestration capacity., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

China has adopted a national carbon emissions trading market to promote emission reductions, but until now, overallocation of allowances suffer low carbon prices and thus to unfulfilled emission reduction goals. We report a general equilibrium modeling that indicates the flexible compliance and price adjustment mechanism of the carbon market, along with explores the solution to the oversupply of allowances in the China's national carbon market. We find that, under the current policy, the initial loose allowance allocation decreases the overall carbon price, and simultaneously the total amount of banked carbon allowances reaches 4.880 billion tons in 2030, resulting in the level of carbon price cannot achieve NDC (Nationally Determined Contribution) targets. However, by introducing carbon market price adjustment schemes, we observe that the cumulative amount of allowances can effectively reduce, enabling the carbon price rising. Importantly, the amount of the supply of allowances decreases most under the benchmark decrease scenario, which increases the emission reduction pressure of the enterprises from the beginning, leading to the largest economic losses, the price-based adjustment mechanism raises the carbon price to expected level at the minimize economic losses, and the quantity-based adjustment mechanism is more sensitive to policy parameters compared to the price -based adjustment mechanism. These findings offer a promising avenue for selecting cost-effective price adjustment mechanism to improve price mechanism design for national carbon markets., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. The policy scenario refers to the flexible mechanism design of the existing China's national ETS, as well as potential adjustment polices to overcome oversupply in the mainstream ETSs. First, the national ETS policy scenario (Current Policy, CP) follows the existing national carbon market policies and only includes the flexible compliance. Second, benchmark value decline scenario (Benchmark Decline, BD) reflects both cap adjustment and flexible compliance. Third quantity-based supply-side adjustment scenario (Quantity-based adjustment, QA) reflects both flexible compliance and price adjustment mechanism, where adjusts carbon prices via flexibly adjusting allowances supply based on the allowance quantity. Fourth, the price-based supply-side adjustment scenario (Price-based adjustment, PA) reflects both flexible compliance and price adjustment mechanism, where adjusts carbon prices via flexibly adjusting allowances supply based on carbon price. The main parameters in the model are shown in Table 2., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Inspection, standing for top-down environmental management practices, also known as campaign-style governance, is used by central governments to lessen local environmental pollution. However, there is no causal evidence for carbon abatement. Employing staggered difference-in-differences (DiD), I find that inspected cities mitigate carbon intensity and carbon emissions by 3.72% and 2.34%, respectively, with economic significance. Conducting a triple difference strategy, I suggest the channels are the local people's congresses and political consultative conferences' proposals, government attention, environmental regulation, industrial structure, and green innovation. Also, the heterogeneous effects suggest that municipal party secretaries assigned to their birthplace, the older the party standing and age, and those with natural sciences majors, are more conducive to the inspection achieving carbon mitigation. An alternative DiD specification shows that the "look-back" inspection achieves sustained carbon reduction. I support the argument that top-down inspection helps achieve resilience to climate change and reduce greenhouse gas emissions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Timely and accurate implementation of carbon emissions (CE) analysis and evaluation is necessary for policymaking and management. However, previous inventories, most of which are yearly, provincial or city, and incomplete, have failed to reflect the spatial variations and monthly trends of CE. Based on nighttime light (NTL) data, statistical data, and land use data, in this study, a high-resolution (1 km × 1 km) monthly inventory of CE was developed using back propagation neural network, and the spatiotemporal variations and impact factors of CE at multiple administrative levels was evaluated using spatial autocorrelation model and spatial econometric model. As a large province in terms of both economy and population, Guangdong is facing the severe emission reduction challenges. Therefore, in this study, Guangdong was taken as a case study to explain the method. The results revealed that CE increased unsteadily in Guangdong from 2013 to 2022. Spatially, the high CE areas were distributed in the Pearl River Delta region such as Guangzhou, Shenzhen, and Dongguan, while the low CE areas were distributed in West and East Guangdong. The Global Moran's I decreased from 2013 to 2022 at the city and county levels, suggesting that the inequality of CE in Guangdong steadily decreased at these two administrative levels. Specifically, at the city level, the Global Moran's I gradually decreased from 0.4067 in 2013 to 0.3531 in 2022. In comparison, at the county level, the trend exhibited a slower decline, from 0.3647 in 2013 to 0.3454 in 2022. Furthermore, the analysis of the impact factors revealed that the relationship between CE and gross domestic product was an inverted U-shaped, suggesting the existence of the inverted U-shaped Environmental Kuznets Curve for CE in Guangdong. In addition, the industrial structure had larger positive impact on CE at the different levels. The method developed in this study provides a perspective for establishing high spatiotemporal resolution CE evaluation through NTL data, and the improved inventory of CE could help understand the spatial-temporal variations of CE and formulate regional-monthly-specific emission reduction policies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

It is crucial for understanding the variations of carbon and nutrient pools within the ecosystems during long-term vegetation restoration to accurately assess the effects of different ecological restoration patterns. However, the long-term spatio-temporal variations of carbon and nutrient pools under different vegetation types remain unclear. The sites for long-term natural and planted forests (i.e., Natural secondary forest, Pinus tabulaeformis planted forest, Platycladus orientalis planted forest, and Robinia pseudoacacia planted forest) on the northeastern Loess Plateau, China were selected, to measure and analyze the differences and interannual variations of vegetation attributes at four synusiae and soil properties at 0-100 cm over the period of 12 years (2006-2017). The principal component analysis (PCA) and Mantel test were also conducted to explore the relationships among vegetation attributes, soil properties, and carbon and nutrient pools. The results showed that: compared with the planted forests, the natural secondary forest had lower arborous biomass (84.21 ± 1.53 t hm -2 ) and higher understory biomass and plant heights. Compared to planted forests, the secondary forest had higher soil carbon and nitrogen contents (13.74 ± 3.50 g kg -1 and 1.16 ± 0.34 g kg -1 ). The soil carbon pool in the secondary forest was 22.0% higher than planted forests, while the vegetation carbon pool in the P. tabulaeformis was 75.5% higher than other forests. Principal component analysis (PCA) and Mantel test revealed that vegetation attributes and soil properties had significant correlations with carbon and nutrient pools, especially at the arborous synusia (p < 0.01). The findings indicated that in the ecologically fragile Loess Plateau region, the selection of appropriate vegetation restoration types should be guided by varying ecological restoration goals and benefits, aiming to expected ecological outcomes. This insight offers a strategic implication for forest management that is tailored to improve carbon and nutrient pools in areas with similar environmental conditions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Variable renewable energy (VRE) is the most promising form of primary generation under a carbon neutrality target due to its environmental benefits, incentive policy, and technological progress. However, the increasing proportion of VRE generation, such as solar and wind power, has sharply increased integration cost and reduced power grid stability. This study uses portfolio theory to investigate China's optimal power generation portfolio by 2050 considering flexibility constraint and system cost, including technical and integration costs. The results demonstrate that non-fossil-fuel power generation technologies have cost and emission reduction advantages over fossil-fuel-based technologies. VRE generation technologies must be developed in synergy with other forms of power generation when considering flexibility requirement and integration cost. A complete phase-out of fossil-fuel power generation technologies in China appears unlikely in the study period. Gas-fired and coal-fired power generation are the pillar forms of power generation to meet future flexibility needs., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier Ltd.)

Sediment associated carbon and nitrogen loss under rainfall, an important cause of soil quality degradation and water eutrophication, strongly depends on the intrinsic properties of original soil types. Relative to total loss, the transport behaviors of organic carbon and nitrogen among sediment size classes and response to soil types remain poorly understood. The concentrations of organic carbon (OC) and total nitrogen (TN) in different sediment size classes (>1, 0.25-1, 0.10-0.25, and <0.10 mm) and their contributions to total sediment load during rainfall erosion were determined under field plot rainfall simulation (at 90 mm h -1 ) on three contrasting soil types (Luvisol, Alisol and Ferralsol) with increased aggregate stability. During rainfall erosion, the concentrations of OC and TN in total and different sized sediments decreased first and then reached a steady state. The variability of OC and TN concentrations (coefficient of variations in 4.2-53.1% and 6.6-41.9%) among sediment size classes decreased from Luvisol to Ferralsol. Compared to original soils, sediments exhibited larger C/N ratios for Luvisol, and smaller values for Alisol, indicating the more selective transport of labile organic matter for weaker aggregated soils. Among sediment size classes, fine particles (<0.10 mm) accounted 69-88% of total OC and TN losses for Luvisol, and decreased to 30-39% for Ferralsol; and the main transport mechanisms of sediment associated OC and TN shifting from suspension-saltation (<0.10 mm) to rolling (>0.25 mm) with increased aggregate stability. Among original soil properties, inorganic cementing agents (including amorphous iron oxides and clay minerals) showed closer relationships with sediment OC and TN losses (|r| = 0.61-0.89, p < 0.001) than organic matter properties (|r| = 0.55-0.87, p < 0.001), further implying the important role of soil aggregate stability across soil types. This study provides an in-depth understanding on soil carbon and nitrogen losses and their divergent characteristics among soil types deserves consideration in the development of erosion model and land management in agricultural systems., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Despite increasing attention to the economic consequences of environmental, social, and governance (ESG) performance, its impact on the quantity and quality of corporate green innovation (GI) remains underexplored. This study aims to reveal the impact and underlying mechanisms of ESG performance on corporate GI using a panel dataset of Chinese-listed enterprises. Our results show that ESG performance increases the quantity and quality of corporate GI by 2.72% and 3.20%, respectively. These significant positive effects are consistent across three ESG sub-ratings and a series of robustness tests, such as the instrumental variable (IV) test based on Confucian culture intensity. Mechanism analysis reveals that ESG performance positively affects corporate GI through the resource effect, governance effect, and innovation effect. Additionally, the GI impact of ESG performance is more pronounced in large, young, growing, and mature enterprises, enterprises in clean and low-carbon industries, and those located in key environmental protection (KEP) and two control zones (TCZ) cities. Our evidence provides insights into the informal drivers of corporate GI and the micro-GI effectiveness of ESG performance in emerging markets like China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

High carbon emissions and population aging are two obstacles to China's development as an emerging economy. It is urgent to scientifically examine the impacts of population aging on carbon emissions to discover new pathways for urban carbon emission reduction. However, existing studies face challenges in terms of focusing on and methodology for accurately capturing the role of industrial production and energy consumption in the environmental effects of population aging. This paper constructs a chain mediation analytical framework and systematically examines the intrinsic linkages between population aging and carbon emissions in Chinese cities from 2000 to 2020 using a two-way fixed effects model. This paper has three main findings. (1) The positive correlation between urban population aging and carbon emissions development is generally low, with coupling coordination degree values of 0.4233, 0.4458, 0.4220, 0.4715, and 0.4665 for each yearly cross-section. (2) For every 1% increase in the population aging rate, carbon emissions decrease by 0.3478% on average. The carbon reduction effect of population aging is significantly greater in low-emissions cities and high-income cities than in other cities. (3) The industrial upgrading (IU) path, energy conservation (EC) path and chain mediation (CM) path account for 72.43%, 7.23% and 20.34%, respectively, of the indirect effects. If the causal link between IU and EC is not considered, the EC effect will be overestimated by 281.16%. The results of the study suggest that properly coping with population aging and reducing carbon emissions are not completely opposed to each other, a conclusion that passes both endogeneity exclusion and robustness check. This paper advocates replacing the one-size-fits-all approach in carbon emissions management and investing more in policy preferences to support carbon emission reduction in both high-emissions and low-income cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The reduction and management of construction waste is crucial for the sustainable development of the construction industry. This research aims to explore a comparative analysis on decoupling relationship between economic growth and construction waste generation on European Union (EU) and Chi et al., 2020 to 2020 in the construction industry, through an integrated method framework of "Tapio + Kaya + LMDI". The research results indicate that there are significant differences in construction waste generation among different countries. The growth rates of construction waste in the EU and China from 2004 to 2020 were 2.47 % and 10.5 %, respectively, showing an upward trend. The economic growth of the construction industry in most EU countries is in a decoupling and negative decoupling state with significant regional differences in decoupling status. The construction waste generation in China is mainly in a weak decoupling state. Economic and demographic factors are the main factors promoting the increase in construction waste generation, while technological factors are the main factors inhibiting construction waste generation in EU and China. However, the impact of each factor on construction was generation varies from EU countries. The research reveals the decoupling effect mechanism between construction waste generation and economic growth, and improves the theory of construction waste management, promotes sustainable development. These findings have feasible inspiration for construction waste management in developing countries with different economic growth levels., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Population aging and global warming have become everyday concerns of all countries. Based on the panel data of 30 provinces in China from 2003 to 2019, this paper uses the panel fixed effect model and two-stage least square method to analyze the effect of population aging on domestic energy carbon emissions of urban and rural residents. On this basis, the threshold regression model is introduced to explore the heterogeneity of the effect under different aging levels. The results show that (1) the progress of population aging at the overall level will significantly increase the level of carbon emissions from household energy consumption. At the regional level, the effect of population aging on carbon emissions from household energy consumption in rural areas is higher than in urban areas. (2) Population aging has a nonlinear effect on the carbon emissions of residential energy consumption. For urban areas, when the level of population aging crosses the threshold, its marginal impact on living carbon emissions in urban areas is further enhanced. In contrast, the opposite is true in rural areas. (3) Heterogeneity analysis results show that the impact of population aging on residential energy carbon emissions differs in different regions at the national and rural levels but does not show regional heterogeneity at the urban level., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Greenhouse gas emissions from the use of fossil energy are the main drivers of global warming. China's dominant consumption of fossil energy necessitates adjustments in its energy consumption structure to break free from the carbon lock-in (CLI) phenomenon. Market-based environmental regulations, represented by the carbon trading market (CTM), play an important role in achieving the dual carbon goals of China. Using panel data of 270 prefecture-level cities in China from 2005 to 2020, this study applies a difference-in-difference model to identify the effect of CTM on urban CLI, analyze its transmission mechanism, and further examine the impact of urban characteristic heterogeneity on policy effects from multiple perspectives. Results show that the construction of CTM significantly reduces the degree of CLI of pilot cities; (2) CTM mainly affects urban CLI by promoting urban green technology innovation, industrial structure upgrading, and public green behavior; and (3) the inhibitory effect of CTM on CLI is more significant in cities with high carbon price, industrialization, and digital finance levels. The primary paths toward realizing carbon unlocking include optimizing the institutional design for CTM, enhancing the effective promotion and application of low-carbon technologies, cultivating the green awareness of the public, and increasing government investments in energy-saving and emission reduction techniques., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Carbon tax and decarbonization subsidy are an effective policy mix in reducing carbon emissions. However, there is a research gap between the deterministic and static analysis related to carbon reduction policy instruments and the dynamic green transition influenced by stochastic factors. This research investigates the optimal dynamic carbon reduction strategies that develop green technologies, increase abatement inputs, and reduce carbon emissions by applying the stochastic optimal control theory. Firms that are incentivized by decarbonization subsidies and regulated by carbon tax choose optimal closed-loop control strategies of abatement inputs to achieve profit-maximizing objectives with carbon reduction constraints. The explicit solutions of the optimal carbon tax and decarbonization subsidy are provided. The simulation results illustrate that the optimal policy mix is feasible in the effective period when the carbon emission decreases significantly, which indicates that the abatement policy mix can effectively promote carbon reduction. Our results reveal that the dynamic optimal policy mix is conducive to achieving carbon abatement goals with capital uncertainty. The government should implement a dynamic carbon tax and decarbonization subsidy policy mix simultaneously associated with optimal closed-loop carbon reduction strategies. Firms with asymmetric decarbonization efficiency can transfer progressively into a cleaner productive pattern., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

This study investigates the impact of China's Low-Carbon Pilot Policy on analyst forecast behaviors. Using a staggered difference-in-difference approach, we find that the program reduced analyst forecast errors and divergence, as mandatory sustainability disclosures enable analysts to incorporate new information to improve forecast quality. The effect is concentrated in state-owned firms and manufacturing-oriented cities. Additional tests explore accounting information quality and investor attention as plausible channels. Overall, exploiting a major Chinese environmental regulation, we provide novel evidence that green policies can enhance transparency and capital market efficiency. The findings offer implications for regulators, investors, and managers as countries pursue climate change mitigation strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Economic policies affect companies' production decisions. And the energy consumption volume is an intuitive reflection of the enterprise's production decisions. In China, coal is the main source of carbon emissions and the most important energy source. Therefore, the coal market and the uncertainty of economic policies are both directly tied to the carbon market. This study explores both the direct impact of economic policy uncertainty and coal price on carbon prices as well as the indirect impact of economic policy uncertainty on carbon prices through coal prices by utilizing the DCC-GARCH model and the NARDL model. The findings indicate that the dynamic correlations between coal prices and the CEPU are always negative and that those between the price of carbon and the CEPU vary by area. Meanwhile, the dynamic correlations between coal and carbon prices are only positive in Shenzhen and Beijing. Both coal prices and economic policy uncertainty produce asymmetrical impacts on carbon prices. Some policy implications are provided for developing the carbon markets in light of the results drawn from the study., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Developing scientific and effective carbon emissions reduction policies relies heavily on precise carbon emission trend prediction. The existing complex spatiotemporal correlation and diverse range of influencing factors associated with multi-regional carbon emissions pose significant challenges to accurately modeling these trends. Under this constraint, this study is inspired by graph learning to establish a hybrid dynamic and static graph-based regional carbon emission network framework, which introduces a novel research standpoint for investigating short-term carbon emissions prediction (CEP). Specifically, a parallel framework of attribute-augmented dynamic multi-modal graph convolutional neural networks (ADMGCN) and temporal convolutional networks with adaptive fusion multi-scale receptive fields (AFMRFTCN) is proposed. The proposed model is evaluated against nineteen state-of-the-art models using daily carbon emission data from 30 regions in China, demonstrating its effectiveness in accurately predicting the trends of multi-regional carbon emissions. Conclusions are drawn as follows: First, especially in regions with marked periodicity, compared with the best baseline model, the mean absolute percentage error (MAPE) of our model is reduced by 20.19%. Second, incorporating graph convolutional neural networks (GCNs) with dynamic and static graphs is advantageous in extracting the spatial features of China's carbon emission network, which are influenced by geographical, economic, and industrial factors. Third, the parallel ADMGCN-AFMRFTCNs framework effectively captures the influence of external information on carbon emissions while mitigating the issue of low prediction accuracy resulting from univariate information. Fourth, the analysis reveals significant differences in the short-term (30-day) growth rate of carbon emissions among different regions. For example, Henan exhibits the highest growth rate (37.38%), while Guizhou has the lowest growth rate (-7.46%). It is valuable for policymakers and stakeholders seeking to identify regions with distinct emission patterns and prioritize mitigation efforts accordingly., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Rapid developments in environmental infrastructure have contributed to significant improvements in green total factor productivity, but further investigation is required to provide a detailed assessment to understand the policy mechanisms involved. This paper analyzes environmental progress in China through MMQR, CCEMG, and AMG as empirical strategies for 30 provinces in China. Our empirical results reveal that energy optimization through renewable energy is the most effective channel to improve green total factor productivity, though it is not the only available option. Since environmental regulations, infrastructure development, and green technology innovation also directly impact energy efficiency, adopting these within policy channels will positively impact environmental sustainability. Our empirical approach helps suggest novel environmental policy suggestions. In particular, policymakers must introduce structural changes within energy developments to foster renewable energy. Furthermore, China must increase environmental spending to upgrade its energy infrastructure further and solve ecological issues. These insights offer valuable policy guidance for decision-makers in China and globally, aiming to foster economic and environmental sustainability and achieve zero-carbon transition goals., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)