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Carbon sequestration is vital to maintaining carbon neutrality and mitigating climate change's impact on humans and the environment. As the government's role in eco-advocacy is primarily designed to safeguard the environment, this paper discusses the impact of government environmental regulations on carbon sequestration. By analyzing 286 cities from 2000 to 2019 in China, we propose that government environmental regulation can lead to a 0.77% increase in carbon sequestration. Additionally, industrial agglomeration and industrial structure advancement are regarded as viable channels. As a result of the differences in city types, government intervention, and public concern across different cities, the impact is likely to vary as well., 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.)

Dual control policies aim to effectively reduce energy consumption and carbon emission by setting targets for total volume and intensity control. With global climate change becoming increasingly serious. China, as a large energy-consuming and carbon-emitting country, faces a huge challenge to reduce carbon emissions. The transition from the "dual control of energy consumption" to the "dual control of carbon emissions" in China is necessary to optimize energy structures, promote energy conservation, reduce carbon emissions, and reach carbon neutrality. This study utilizes multi-regional input-output models to evaluate the results toward both goals from production-based, consumption-based, and income-based perspectives. The findings indicate that "dual control of carbon emissions" is a more precise approach than "dual control of energy consumption". Some provinces have met the latter goal while still falling short of the former. Provinces having met their production-based energy intensity and carbon emission intensity targets, but having failed their consumption-based and income-based targets, are identified to develop a comprehensive and accurate assessment of these targets. A net outflow of embodied energy and carbon emissions is observed among provinces in less-developed central and southern regions to more-developed eastern and northern regions. Suggestions and policy implications based on these findings include establishing a comprehensive evaluation of energy and carbon intensity, considering both consumption-based and income-based perspectives, as well as facilitating enhanced cooperation among developed and developing provinces., 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.)

Terrestrial Water Storage (TWS) plays a pivotal role in water resource management by providing a comprehensive measure of both surface water and groundwater availability. This study investigates changes in TWS driven by human activities from 2003 to 2023, and forecasts future TWS trends under various climate change and development scenarios. Our findings reveal a continuous decline in China's TWS since 2003, with an average annual decrease of approximately 1.36 mm. This reduction is primarily attributed to the combined effects of climate change and human activities, including irrigation, industrial water use, and domestic water consumption. Notably, TWS exhibits significant seasonal and annual fluctuations, with variations ranging ±10 mm. For the future period (2024-2030), we project greater disparities between water resource supply and demand in specific years for the Songliao, Southwest, and Yangtze basins. Consequently, future water resource management must prioritize water conservation during wet seasons, particularly in years when supply-demand conflicts for limited water resources intensify. This study is valuable for effective planning and sustainable utilization of water resources., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Chunmiao Zheng reports financial support was provided by the Ministry of Education of China and the National Natural Science Foundation of China. If there are other authors, they 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.)

Increased ecological land (IEL) such as forests and grasslands can greatly enhance ecosystem carbon sinks. Understanding the mechanisms for the magnitude of IEL-induced ecosystem carbon sinks is crucial for achieving carbon neutrality. We estimated the impact of IEL, specifically the increase in forests and grasslands, as well as global changes including atmospheric CO 2 concentration, nitrogen deposition, and climate change on net ecosystem productivity (NEP) in National Key Ecological Function Zones (NKEFZs) in China using a calibrated ecological process model. The NEP in NKEFZs in China was calculated to be 119.4 Tg C yr -1 , showing an increase of 42.6 Tg C yr -1 from 2001 to 2021. Compared to the slight contributions of climate change (-8.0%), nitrogen deposition (11.5%), and reduction in ecological land (-3.5%), the increase in NEP was primarily attributed to CO 2 (66.5%) and IEL (33.5%). Moreover, the effect of IEL (14.8 Tg C yr -1 ) surpassed that of global change (13.1 Tg C yr -1 ) in the land use change zone. The IEL-induced NEP is significantly associated with CO 2 fertilization, regulated by precipitation and nitrogen deposition. The high values of IEL-induced NEP occurred in areas with precipitation exceeding 800 mm and nitrogen deposition exceeding 25 kg N ha -1 yr -1 . We recommend prioritizing the expansion of ecological land in areas with sufficient water and nutrients to enhance CO 2 fertilization, while avoiding increasing ecological land in regions facing unfavorable climate change conditions. This study serves as a foundation for comprehending the NEP response to ecological restoration and global change., 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.)

Many studies have confirmed that climate change leads to frequent urban flooding, which can lead to significant socioeconomic repercussions. However, most existing studies have not evaluated the impacts of climate change on urban flood from both event-scale and annual-scale dimensions. In addition, there are only few studies that simultaneously consider scenario and model uncertainties of climate change, and combine flood risk assessment and uncertainty analysis results to provide practical suggestions for urban drainage system management. This study uses the statistical downscaling method to calculate the design rainfall under ten rainfall return periods of four climate models and three climate change scenarios in 2040s, 2060s, and 2080s in various prefecture-level cities in China. The four climate models are HadGEM2- ES, MPI-ESM-MR, NorESM1-M and FGOALS-g2 models and the three climate change scenarios are constructed by different representative concentration pathways (RCP), namely RCP2.6, RCP4.5 and RCP8.5. On this basis, relying on the generated drainage systems using geographical information and other data, event-scale and annual-scale precipitation are combined to calculate the change ratio of annual flood volume expectation in prefecture-level cities in each future year compared with the current situation. Furthermore, the study evaluates scenario and model uncertainties of climate change, and then comprehensively integrates the flood risk and its uncertainties to provides suggestions for urban drainage system management., 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.)

Climate change is considered one of the major systemic risks facing the world in the 21st century. To address climate change, China has adopted a series of climate policies, but the uncertainty brought about by frequent climate policy issuance has increased pressure on enterprises, which may not be conducive to enterprises reducing emissions. This paper uses data on 1211 listed companies on the A-share market in China from 2012 to 2022 to study the impact of climate policy uncertainty on enterprise pollutant emissions. The research findings show that climate policy uncertainty increases corporate pollution emissions; climate policy uncertainty mainly generates negative impacts on enterprise environmental regulation, social responsibility, and R&D investment, thereby negatively affecting enterprise emissions reduction. Further heterogeneity analysis shows that climate policy uncertainty in China has a more significant impact on non-state-owned enterprises, technology-intensive enterprises, lightly polluting enterprises, and enterprises in western regions. These findings emphasize the importance of enterprise social responsibility, environmental regulation, and R&D investment in enterprise emissions reduction and provide policy implications for Chinese enterprises to optimize their energy-saving and emission reduction strategies in the face of climate policy uncertainty., Competing Interests: Declaration of competing interest All authors disclosed no relevant relationships., (Copyright © 2024. Published by Elsevier Ltd.)

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.)

Influence of climate change on the geospatial heterogeneity in agricultural production remains poorly understood. In this study, heterogeneity in climate's impacts on wheat production across the North China Plain (NCP) was explored by integrating APSIM model, process-based factor-control quantitative approach, and geostatistical analyses. The results indicated that increased precipitation and minimum temperature boosted yields, while elevated maximum temperature and reduced radiation exerted adverse effects. The most pronounced negative impact arose from the coupling variation between maximum temperature and radiation, contributing to yields' variations of -5.84% from 2000 to 2010 and -5.22% from 2010 to 2020. In last two decades, climate change has augmented the overall geospatial heterogeneity degree in wheat yields. The chief factor contributing to yields' heterogeneity was the maximum temperature during anthesis-maturation stage, explaining an average of 37.6% of yields' heterogeneity, followed by precipitation throughout the whole growth period and the anthesis-maturation stage, explaining 36.1% and 34.5% respectively. A reciprocal enhancement mechanism exists between factors in driving yields' heterogeneity. Wheat yields in the southwestern NCP benefited more from increased precipitation and minimum temperature. Between 2000 and 2010, yields in the central NCP (junctions of Henan, Hebei, and Shandong) experienced the most pronounced adverse impact from increased maximum temperature. However, by 2010-2020, significant adverse impact shifted to western NCP, expanding spatially. During 2010-2020, the geospatial scope of radiation's significant negative impact expanded compared to the preceding decade, particularly affecting the yields in central and eastern NCP. The identified geospatial heterogeneity pattern of climate's impacts can guide spatially-matched climate-adaptive management adjustments. For instance, intensifying the defense against high-temperature's impacts in northwestern Henan, southern Hebei, and western Shandong, while improving the adaptation to radiation reduction in the central and eastern NCP. The findings are expected to advance regional-scale climate-smart agricultural 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.)

Climate change has profoundly affected human production and development, and also brought new challenges to China's goal of achieving inclusive economic growth. Using the data of 273 cities in China from 2001 to 2019, this research evaluates the impact of climate change on urban inclusive economic growth by constructing a temperature bin variable according to the daily average temperature. It is found that with the temperature bin [18 °C, 21 °C) as the benchmark group, both temperature rise and fall have a negative influence on inclusive economic growth. Notably, the adverse effects of high temperatures (above 27 °C) are statistically and economically significant, exhibiting a trend of increasing magnitude. The mechanism test shows that high temperature affects the inclusive growth level of the urban economy mainly by exacerbating the urban‒rural income gap. The heterogeneity analysis found that cities located in hot regions, southern regions or coastal areas are more sensitive to high temperatures and experience more prominent effects. This research holds significant practical implications for China to achieve a win‒win situation of balanced economic development and climate governance., 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.)

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.)

Enterprise digital transformation (EDT) is a strategic initiative that provides robust support for optimising resource allocation, fosters business innovation, and significantly impacts ecological environment to increase financial performance. This study re-examines the substantial contributions of EDT to climate change mitigation. Drawing on data from Chinese A-share listed companies from 2010 to 2021, we investigated the changes and mechanisms influencing carbon emissions reduction performance (CERP) of enterprises undergoing digital transformation. The empirical results indicate that EDT actively contributes to enhancing the CERP of enterprises, with a more pronounced effect observed in non-polluting industries, state-owned enterprises, and manufacturing companies. Furthermore, empirical findings from mechanism tests reveal that EDT effectively improves the CERP by driving green technological innovation, strengthening industry chain connections, and enhancing capacity utilisation. Finally, within external oversight groups, particularly in government and investor supervision, the enhancement of enterprise CERP is more significant, highlighting the crucial role of external oversight in the EDT process., 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.)

Adapting to climate change is critical to building sustainable and resilient agricultural systems. Understanding farmers' perceptions of climate change has become the key to the effective implementation of climate change adaptation policies. This research draws multidisciplinary attention to how farmers participate in decision-making on adaptation behaviors and provides useful insights for realizing synergies between environmental change and agricultural production. In this work, we conducted a meta-analysis of 63 quantitative studies on Chinese farmers' adaptation to climate change to assess the relationship between motivational factors and adaptation behavior. Our analysis highlights that farmers' perceptions of precipitation changes are often inaccurate; however, other psychological factors, such as perception, experience, and risk attitude, significantly positively impact their adaptation behavior. In addition, different climate regions are the main source of high heterogeneity in inter-study comparisons of climate change perception, and the effect of climate regions may therefore constitute a moderating factor that weakens the positive relationship between climate change perception and adaptive behavior. Furthermore, this study highlights the need to intervene at the household level to enhance farmers' adaptability to climate change, which includes providing support through income diversification, early warning information services, training, assistance, credit, subsidies, and other resources. In the future, research on how perception, experience, and risk interact to affect adaptive behavior should be strengthened., 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.)

Ecological restoration projects (ERPs) are implemented worldwide to restore degraded ecosystems and promote ecosystem sustainability. In recent years, a series of ERPs have been implemented to enhance vegetation cover in the unique alpine ecosystems of the Qinghai-Tibet Plateau (QTP). However, the current assessment of the ecological benefits of ERPs is relatively single, and the scale and extent of future ecological restoration project implementation cannot be determined. We quantified trends in normalized vegetation index (NDVI) since the implementation of ERPs. Changes in four major ecosystem services were assessed before and after ERPs implementation, including wind erosion protection, soil retention, water yield, and net primary productivity (NPP). The relationship between NDVI and ecosystem services was further explored using a constraint line approach to identify NDVI as a threshold reference for ERPs implementation. The results showed that: (1) since the implementation of ERPs, 21.80% of the regional NDVI of the QTP has increased significantly. (2) After the implementation of ERPs, the average total ecosystem services index (TES) increased from 0.269 in 2000 to 0.285 in 2020. The average soil retention and water yield increased but the NPP and sandstorm prevention decreased slightly. (3) NDVI had no significant constraint effect on soil retention and NPP, but there was a significant constraint effect on wind erosion prevention and water yield. (4) The constraint line of NDVI on TES was S-shaped. After the implementation of ERPs, the TES gradually reached a threshold value when NDVI was 0.65-0.75. Our findings identify significant contributions of ERPs and thresholds for the constraining effects of vegetation cover on ecosystem services, which can inform sustainable ERPs for governments., 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.)

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.)

With global warming, cities are vulnerable to extreme weather, increasing the climate risk to cities worldwide. Although existing literature has examined the ex-post impacts of extreme weather, it is less clear how climate risk affects cites before extreme weather occur. To lower the risk of urban waterlogging, which is caused by extreme weather, and improve the ability of cities to adapt to extreme weather, China launched the Sponge City Project (SCP) in 2013 to manage the urban stormwater and waterlogging. Adopting the SCP pilot in China as a quasi-natural experiment, we examine the impact of the climate risk caused by urban stormwater and waterlogging on the house price with the difference-in-differences (DID) method. We find that after implementing the SCP pilot program, the house price in pilot cities increased significantly because of the improvement in cities' resilience to climate risk. Additionally, this effect was only demonstrated in cities with a high waterlogging risk. For SCP pilot cities with lower waterlogging risk, the house price is not significantly affected by SCP implementation. This indicates that the house price in China is sensitive to the climate risk caused by the urban stormwater and waterlogging. Our findings also contribute to the understanding of the significance of the climate risk management, and provided theoretical evidence for urban governance., 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 © 2023 Elsevier Ltd. All rights reserved.)

Climate change is considered to increase economic costs by worsening heat-related labor productivity loss. While extensive global and national research has been conducted on this topic, few studies have analyzed subnational and individual economic impacts, potentially weakening local governments' motivation to tackle climate change. Figuring out the most affected regions and labors could help climate policymakers to identify priority regions and sectors to allocate adaptation resources efficiently, and enhance stakeholder engagement. This study adopted a provincial Computable General Equilibrium model by distinguishing different labors and regions in modelling work to address the aforementioned gap. The study estimated economic costs at different level under three climate change scenarios (lower (SSP126), middle (SSP245), and higher (SSP585) warming scenario). Low-income regions located in southwest part of China (such as Guangxi and Guizhou), would experience the largest economic loss, 3.4-7.1 times higher than high-income in China by 2100 under SSP245 scenario. Additionally, wages for labors highly sensitive to heat in these regions are expected to rise, for example, by an 8.3% rise in Guangxi, driven by the rising demand for these labors. Conversely, others would experience a significant wage decrease, especially those with less sensitivity (e.g., managers). Therefore, we recommended that national financial supports be allocated more to these most affected regions and that government encourage managers provide assistance to workers vulnerable to heat., 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 © 2023. Published by Elsevier Ltd.)

Cities generate substantial carbon emissions while facing serious climate challenges. The construction of low-carbon cities is a vital measure to cope with climate change. China has implemented three batches of low-carbon city pilots since 2010, with advocating low-carbon lifestyles and consumption patterns for residents as an indispensable component. However, there is limited research on the impact of pilot cities on carbon emissions of residents, as well as a lack of theoretical guidance and accurate data. Based on 281 prefectural-level cities in China, this study established the difference-in-differences model to evaluate the impact of low-carbon city pilots on residents' carbon emissions. By introducing the Attitudes, Behaviours, and Circumstances theory as the analytical framework and compiling a direct carbon emission inventory of residents, this study attempts to fill the research gap. The results indicate that low-carbon city pilots could lower residents' carbon emissions by 7.3% annually, with a gradual strengthening trend over time. This study presents and validates three influencing mechanisms of the pilot policy, including improving green attitudes, advocating low-carbon behaviours, and optimizing living circumstances. The heterogeneity analysis demonstrates that the eastern and central regions with higher levels of economic development have generated significant policy effects. Although both the northern and southern pilot cities have lowered residents' carbon emissions, the former has exhibited a stronger policy effect. Based on these findings, this study proposes several policy recommendations: innovate attitudinal guidance, nudge behavioural choices, shape external circumstances, and combine local characteristics., 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 © 2023 Elsevier Ltd. All rights reserved.)

Climate change has resulted in great influence on the geographical distribution of species. Mangrove forests are one of the most precious ecosystems on the planet, yet they are being threatened by the habitat destruction and degradation under the situation of global warming. Seeking suitable areas for planting mangroves to tackle climate change has been gradually popular in ecological restoration. In this study, we applied the Maximum Entropy algorithm to assess the contribution of environmental factors on mangrove distribution, simulated mangrove suitable habitat for present and future (scenario of SSP245-2070s), and used kernel density analysis for identifying priority of mangrove reserve construction. Results indicate that mean diurnal range and elevation made the highest contribution on mangrove distribution. At present, the mangrove habitat suitability along the western coast of the Guangdong-Hong Kong-Macao Area (GHMA) was the highest while that along the eastern coast was the lowest. By 2070s, mangrove suitable areas would show a decreasing trend under SSP245 scenario. High suitable areas (HSAs) would change fastest and shift to northeast in the same direction as dominant environmental factors. For further mangrove restoration, it is advisable to select sites with high suitability density in the future but low reclamation density at present as prior mangrove reserves, and these sites distribute along the northeastern and northwestern coast of Zhanjiang, Yangjiang and Jiangmen, the Pearl River Estuary and Honghai Bay of Shanwei. Meanwhile, regions with lower suitability density but higher reclamation density could be listed as secondary mangrove reserves., 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 © 2023 Elsevier Ltd. All rights reserved.)

Climate change has increasingly become a significant challenge to sustainable socio-economic development, and climate adaptation is a key issue that relevant research focuses on regional sustainable development models. By employing panel data between 2007 and 2020 from 284 Chinese prefecture-level cities, this study adopts quasi-experimental methods, including a difference-in-differences design and double dual machine learning model, to study the impact of climate adaptability on green regional sustainable development. Empirical results confirm that the pilot policy of building climate-resilient cities significantly improves urban green total-factor productivity. Difference-in-difference models (derived from entropy-weight and propensity score matching) and double dual learning models also support the improving effect of regional green total-factor productivity after policy intervention. The digital economy has strengthened the green development effect of pilot policies for building climate-adaptive cities. In addition, policy interventions to build climate-adaptive cities promote green urban development by optimizing industrial development structures and enhancing economic growth resilience. In addition, climate adaptability can also attract highly skilled talent and high-quality enterprises, facilitate science and technological progress in urban areas, and thus promoting the green development of cities in China. This study objectively evaluates the effects of climate policies and provides insights for global adaptation to climate change and optimization of public policies., Competing Interests: Declaration of competing interest The authors declare no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

The effects of global climate change and human activities are anticipated to significantly impact ecosystem services (ESs), particularly in urban agglomerations of arid regions. This paper proposes a framework integrating the dynamic Bayesian network (DBN), system dynamics (SD) model, patch generation land use simulation (PLUS) model, and the Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model for predicting land use change and optimizing ESs spatial patterns that is built upon the SSP-RCP scenarios from CMIP6. This framework is applied to the oasis urban agglomeration on the northern slope of the Tianshan Mountains in Xinjiang (UANSTM), China. The findings indicate that both the SD model and PLUS model can accurately forecast the distribution of future land use. The SD model shows a relative error of less than 2.32%, while the PLUS model demonstrates a Kappa coefficient of 0.89. The land use pattern displays obvious spatial heterogeneity under different climate scenarios. The expansion of cultivated land and construction land is the main form of land use change in UANSTM in the future. The DBN model proficiently simulates the interactive relationships between ESs and diverse factors. The classification error rates for net primary productivity (NPP), habitat quality (HQ), water yield (WY), and soil retention (SR) are 20.04%, 3.47%, 4.45%, and 13.42%, respectively. The prediction and diagnosis of DBN determine the optimal ESs development scenario and the optimal ESs region in the study area. It is found that the majority of ESs in UANSTM are predominantly influenced by natural factors with the exception of HQ. The socio-economic development plays a minor role in such urban agglomerations. This study offers significant insights that can contribute to the fields of ecological protection and land use planning in arid urban agglomerations worldwide., 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 © 2023 Elsevier Ltd. All rights reserved.)

Comprehending endangered species' spatial distribution in response to global climate change (GCC) is of great importance for formulating adaptive management, conservation, and restoration plans. However, it is regrettable that previous studies mainly focused on geoclimatic species, while neglected climate-sensitive subterranean taxa to a large extent, which clearly hampered the discovery of universal principles. In view of this, taking the endemic troglophile riverine fish Onychostoma macrolepis (Bleeker, 1871) as an example, we constructed a MaxEnt (maximum-entropy) model to predict how the spatial distribution of this endangered fish would respond to future climate changes (three Global Climate Models × two Shared Socio-economic Pathways × three future time nodes) based on painstakingly collected species occurrence data and a set of bioclimatic variables, including WorldClim and ENVIREM. Model results showed that variables related to temperature rather than precipitation were more important in determining the geographic distribution of this rare and endemic fish. In addition, the suitable areas and their distribution centroids of O. macrolepis would shrink (average: 20,901.75 km 2 ) and move toward the northeast or northwest within the study area (i.e. China). Linking our results with this species' limited dispersion potential and unique habitat requirements (i.e. karst landform is essential), we thus recommended in situ conservation to protect this relict., 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 © 2023 Elsevier Ltd. All rights reserved.)

Carbon Intensity Constraint Policies (CICPs) are vital for addressing climate change challenges and advancing sustainable development. Since 2010, China has rolled out three five-year CICPs. However, there is limited understanding of their impact on carbon emission performance (CEP). Addressing this, this study pioneers the exploration of the CICP's impact on China's CEP. Drawing from government intervention and green paradox theories, this study highlights a concerning scenario: local governments achieve emission targets via excessive intervention. For deeper insights, this study melds the overall technology frontier concept with a non-radial, non-angle directional distance function, introducing a novel efficiency model rooted in the Data Envelopment Analysis (DEA) method. This offers a CEP measure across 30 Chinese provincial regions from 2002 to 2019. Using the quasi-difference-in-differences (quasi-DID) and moderated mediation models, this study ascertains the presence of the green paradox, uncovers its reasons, and suggests mitigation strategies. The results indicate that high government intervention diminishes CEP. This negative effect intensifies under greater regional fiscal pressure. Alarmingly, local authorities' eagerness to meet targets shows a counterproductive, inverted N-shaped trend regarding CICPs' time-based influence on regional CEP. Moreover, the impact varies based on regional economic development levels and stages. This study has ensured the robustness of the findings via parallel trend tests, parallel exclusion policies, a strengthened quasi-DID framework, and diverse control variable configurations. This study underscores the need for more balanced government intervention. It offers valuable policy insights, guiding China's upcoming CICP phase to realize the ambition of peaking carbon by 2030 and achieving carbon neutrality by 2060., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financialinterestsor personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 Elsevier Ltd. All rights reserved.)

Climate change is altering urban rainfall characteristics, leading to extreme urban stormwater and, particularly, more frequent flooding. Due to the uncertainty of climate change, the responses of urban drainage systems to climate change are becoming more complicated. This complexity makes it difficult for decision makers to assess whether urban infrastructure is sufficiently resilient to cope with flood risks. In this study, the Xiao Zhai area, a high-density urban area of China, was used as an example. A quantitative method for assessing these risks and the resilience of urban drainage systems to future urban stormwater was developed. First, based on the Coupled Model Intercomparison Project Phase 6 (CMIP6), the variation and uncertainty of future rainfall in the study area were analysed. A high-fidelity hydro-hydraulic model was developed to analyse the influence of climate change on future urban stormwater. Finally, the relationship between urban flood risk and the resilience of urban drainage systems was evaluated. The results show that the temporal distribution of future rainfall from 2023 to 2100 is relatively uniform. However, the number of heavy rainfall events increases significantly during this period. The flood risk caused by future rainfall was one level higher than the historical flood risk. For example, the flood risk caused by future 5a rainfall is equal to the flood risk from historical 10a rainfall. The correlations between the spatial distributions of flood risk and resilience are 0.49-0.63. Urban drainage systems urgently need to be improved and refined in areas with flood risk and low resilience to become more resilient to climate change. Rational planning of grey-green rainwater facilities in flood risk and low resilience areas can improve the rainwater system's resilience to 0.67-0.95 for climate change., 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 © 2023 Elsevier Ltd. All rights reserved.)

Industrial structure adjustment is an effective way to reduce carbon emissions. When adjusting the industrial structure, changes in the input and output of industrial sectors in one region may cause changes in industrial sectors of other regions, resulting in inter-regional industrial linkages. What is the impact of industrial structure adjustment on the linkages, and how does it impact climate change? The modified gravity model combines the influence and inductive coefficients to build the carbon emission industrial spatial linkage network (ISL) using social network analysis (SNA) in this work with the data of China's 2008-2019 provincial carbon emissions. The impact of industrial structure adjustment on carbon emission industrial spatial linkage network and its effect on climate change mitigation are explored with the help of the STIRPAT model and quality assurance procedure (QAP). The results of this study are as follows: (1) The provincial carbon emissions under industrial spatial linkages show significant network characteristics, the network density increased from 0.1437 in 2008 to 0.323 in 2019. (2) There is a significant spatial spillover effect in the industrial spatial linkage network (2019) centered on Shandong and Jiangsu. The provinces can be divided into 4 blocks, which play different roles in the network. (3) The industrial structure upgrading can significantly promote the carbon emission industrial linkage network and mitigate climate change. (4) The industrial structure rationalization can promote the development of the network and mitigate climate change in the early and late development stages of carbon emissions industrial linkage. In addition, it is necessary for the government to pay more attention to the development level of the carbon emission spatial industrial linkage network when implementing industrial structure adjustment in response to climate change., 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 © 2023. Published by Elsevier Ltd.)

G20 countries, account for the majority of global carbon emissions, need to take the lead in mitigating climate change. However, there are differences in economic, political and sociocultural backgrounds amongst G20 countries, especially between China and other G20 countries. Since the implementation of climate policies largely require domestic public support, it is vital to explore similarities and differences of public opinions on climate issues between China and other G20 countries. However, little research has investigated the micro-level climate concerns from the perspective of cross-country differences. Therefore, based on big data and text mining analysis, this study crawled user-generated data on Sina Weibo (N = 271,487) and Twitter (N = 4,874,546) from 2008 to 2019 to comprehensively catalog and compare climate opinions. Results show that climate change has become a salient issue in China and other G20 countries, with climate-related surges in public opinions always occurring after major natural, social and political events. Moreover, in China, there has been a significant shift in public attention from climate impacts to climate mitigation strategies, but in other G20 countries, people are more radical by emphasizing the climate movement and calling for authoritative actions. This study provides "snapshots" of climate communication and offers a quantification-based reference for promoting climate actions and collaborative governance., 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 © 2023 Elsevier Ltd. All rights reserved.)

The Chengbi River Basin is a typical karst watershed in Southwest China. Understanding the effects of climate change (CC) and human activities (HAs) on hydrological process is important for regional water resources management and water security. However, a comprehensive assessment of the effects of CC and HAs on runoff dynamics at different time scales in the Chengbi River Basin is still lacking. To address these needs, we used Budyko Mezentsev-Choudhurdy-Yang and Slope change ratio of accumulative quantity methods to assess the contribution of the changing environment to annual and intra-annual runoff changes in the Chengbi River Basin. The results indicated that annual runoff time series was divided into the base phase Ta (1980-1996) and the change phase Tb (1997-2019). Compared to the natural status in Ta, the relative contributions of CC and HAs to the runoff increase in Tb were 154.86% and -54.86%. In addition, the shift in intra-annual runoff occurred in 2007 and was mainly caused by HAs, with a contribution rate of 76.22%. The increase in annual runoff in Tb could be attributed to the positive contribution of rainfall. Changes in rainfall and reservoir construction altered the original state of intra-annual runoff. Furthermore, the high degree of heterogeneity in the surface karst zone increased the runoff coefficient. The spatial unsaturation of the subsurface water-bearing media and rainfall patterns caused a significant lag effect in the response of surface runoff to rainfall. This study can help researchers and policy makers to better understand the response of karst runoff to changing environment and provide insights for future water resources management and flood control measures., 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 © 2023 Elsevier Ltd. All rights reserved.)

Clean energy conversion is a core approach and development trend to tackle climate change, while the severe drawbacks such as supply deficiency and cost increase restrict regional sustainable development. This paper employs a natural experiment of coal-to-gas conversion of the Chinese government to study the effect of such policy on regional sustainable development, as well as the underlying mechanism. Based on a city-level dataset from 2006 to 2019, this paper measure green total factor productivity (GTFP) using data envelopment analysis (DEA) combined with the Malmquist‒Luenberger productivity index. Then, this paper evaluates the impact of the CTG policy in pilot cities using the Difference-in-Difference (DID) with Propensity Score Matching (PSM) approach. This paper finds that the CTG policy increased the GTFP of the pilot cities by 2.25% (0.0229/1.02). A series of robustness tests confirmed the findings. Subsequent mechanism analysis shows that the CTG policy increases the GTFP of pilot cities mainly by increasing technical efficiency. In addition, the mechanism of the CTG policy's impact differs between central and noncentral cities. In particular, the CTG policy increases the technological innovation indicator (TC) of provincial capital cities by 2.35% while it increases the technical efficiency indicator (EC) of other cities by 1.89%, which proves the Porter effect in provincial capital cities. Finally, several implications are provided for policymakers to promote other types of renewable energy., 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 © 2023 Elsevier Ltd. All rights reserved.)

An ambitious new Post-2020 Global Biodiversity Framework "Kunming-Montreal Global Biodiversity Framework" has been developed. However, the combined effects of climate change and human modification can undermine the potential benefits of the global post-2020 conservation efforts. The co-benefits of stabilizing the climate, conserving biodiversity, and maintaining intact wilderness areas may help to persuade the general public of the need to quickly expand existing protected areas (PAs). To maximize the co-benefits after 2020, the careful optimization of existing (PAs) network and scientific identification of conservation targets are both essential. Here, we mapped hotspots of biodiversity, climate vulnerability, and wilderness in Southwest China (SWC). By analyzing the representativeness and gaps of the existing PAs network in SWC, we devised post-2020 conservation targets and highlighted their implications for decision-makers. Our results showed that the incongruence between hotspots of different species exists, indicating that habitats suitable for one taxon may not fully harbor other taxa. According to our assessment, the five jurisdictions of SWC have warmed on average by 0.4°C-1.1 °C over the past 60 years alone. In particular, biodiversity hotspots in SWC are undergoing stark climatic changes. We uncovered prominent conservation gaps in SWC's network of PAs, especially in terms of climate vulnerability and biodiversity. Due to their insufficient number and unreasonable spatial distribution, the PAs network in SWC may be not capable of meeting its biodiversity, climate vulnerability, and wilderness conservation objectives. To rectify this, we proposed a 3-step mission: milestone 2025, milestone 2030, and goal 2050, which aims to protect 23%, 28%, and 60% of the terrestrial area in SWC, respectively. Taken together, our study derived conservation priority areas with relatively clear spatial boundaries and importance levels, thus providing detailed, timely information for decision-makers to expand the PAs network and implement conservation measures varying in strictness in post-2020 conservation practice., 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 © 2022 Elsevier Ltd. All rights reserved.)

The Budyko-based elasticity method has been widely employed to clarify the driving factors behind runoff changes. However, different formulations of the Budyko framework could produce biases in the elasticity analysis and the assessment errors induced from different formulations of the Budyko framework in the elasticity method remain unclear. Here, we attempt to address this issue by validating the performance of elasticity methods derived from two analytical Budyko equations (Fu's equation and Choudhury's equation), as well as one empirical Budyko equation (Wang-Tang's equation) of the Budyko framework across 22 basins in China. Validations show that the runoff change simulated by the elasticity method derived from the empirical equation has lower errors compared with the two analytical Budyko equations. Results reveal that in the semi-humid environment, the alteration of basin characteristics takes the main responsibility for the runoff change. However, a clear divergence was found in simulated runoff changes among different Budyko-based elasticity methods in humid basins. For parts of the humid basin, the precipitation is the main driver of runoff change from the analytical Budyko-based elasticity methods, while the alteration of basin characteristics is the main derive of the runoff changes according to based on the empirical Budyko-based elasticity method. This difference could be attributed to the variations in the simulated contributions from the alteration of basin characteristics on runoff changes. Generally, our results highlight the importance of validating different Budyko equations when applying the elasticity method to investigate the driver of the runoff changes in humid regions., 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 © 2022 Elsevier Ltd. All rights reserved.)

Global climate change is rooted in the imbalance between carbon sources and sinks, and net-zero greenhouse gas emissions should focus not only on the source-side drivers but also on the sink-side influencing factors. Taking the county-level administrative districts in China as the sample, this study uses machine learning models to fit the relationship between socioeconomic development (SED) and net primary productivity (NPP) of terrestrial ecosystems. Moreover, it identifies key influencing factors and their effects based on the SHapley Additive exPlanations (SHAP) algorithm. The results show that the districts with low terrestrial NPP show the characteristics of agglomeration distribution. The eight key factors, in order, are as follows: agricultural development level, latitude, population size, longitude, animal husbandry development level, economic scale, time trend and industrialization level. In this study, via SHAP interaction plots, we found that the effects of population, economic growth, and industrialization on terrestrial NPP are regionally heterogeneous; via cluster analysis, we found the stage characteristics of the mode of SED affecting terrestrial NPP. Therefore, the conservation of terrestrial NPP needs to be combined with the stage changes of SED, as well as inter-regional differences, to develop a regionally coordinated and time-coherent ecological carbon sink conservation plan., 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 © 2022 Elsevier Ltd. All rights reserved.)

Vegetation change reflects sensitive responses of ecosystem environment to global climate change as well as land use. It is well known that land use type and its transformation affect vegetation change. However, how the changes in land use intensity (LUI) within different land use types impact vegetation and the interactions with other drivers remain poorly understood. We measured the LUI of Jiangsu Province, China, within the main land use types in 1995, 2000, 2005, 2010, 2015 and 2018 by combining remote sensing-based land use data with representative county scale economic and social indicators. Structural equation models (SEMs) were built to quantify the influences of long term LUI on vegetation change interacting with economic development, climate change and topographical conditions in transformed land, cropland, rural settlements and urbanized land, respectively. Seventy percent of significant vegetation change existed in non-transformed land use types. Although the area with a vegetation greening trend is larger than that with a vegetation browning trend, the vegetation browning areas is prominent in urbanized lands and some croplands in south basins. The constructed SEMs suggested the dominant negative effect of fast economic development regardless of land use types, while LUI played important and different direct and indirect effects on affecting vegetation change significantly interacting with economic development and climate change in different land use types. The LUI increasing led a vegetation greening in cropland, and stronger than climate warming with both positive direct and indirect effects for influencing climate change. The LUI change took negative effects on vegetation change in rural and urban areas, while a positive indirect effect of LUI increasing in urbanized land signaled the positive results of human managements. We then provided some land use-specific suggestions on basin scale for land management in Jiangsu. Our results highlight the necessity of long-term LUI quantification and promote the understanding of its effects on vegetation change interacted with other drivers within different land use types. This can be very helpful for sustainable land use and managements in regions with fast economic 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 © 2022 Elsevier Ltd. All rights reserved.)

The impact of climate change on nearshore coastal water quality and its interaction with pollution prevention efforts (e.g., the development of green and gray water infrastructure) still lack systematic investigation. This study performed a holistic analysis of the impact of climate change on the salinity and concentrations of total nitrogen (TN), total phosphorus (TP) and chlorophyll a (Chl.a) in Shenzhen Bay between Shenzhen and Hong Kong, the two most developed megacities in South China, based on three-dimensional hydrodynamic and water quality modeling. The major study findings were as follows. First, Chl.a was the most sensitive parameter, and its bay-wide average concentration in 2100 was predicted to be approximately 13% and 46% higher than those in 2015 under mild and rapid climate change scenarios, respectively. Second, sea level rise was found to be a major driver of all four water quality parameters, while temperature and radiation mainly influenced Chl.a and precipitation mainly influenced nutrients. Third, water quality responses to climate change were highly heterogeneous over the bay. Even under a mild climate change scenario, the highest location-specific changes (2100 vs. 2015) in salinity and TN, TP and Chl.a concentrations were projected to be approximately 21%, 19%, 25%, and 65%, respectively. Fourth, changes in seasonal variation due to climate change may lead to an enhanced ecological risk of algal blooms. Finally, the effect of reducing TN and TP concentrations by proposed water infrastructure development was found to be significantly weakened (nearly 40% and 20% for TN and TP, respectively, under a mild climate change scenario), while the negative effect (i.e., increase in the Chl.a concentration) was notably accelerated. Regional cooperation is critical for protecting the water quality of the bay, particularly under climate change. The insights obtained in this study are applicable to other coastal water zones around the world with similar socioeconomic backgrounds and climatic 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 © 2022 Elsevier Ltd. All rights reserved.)

Ecological engineering is an important measure to promote ecosystem adaptation and restoration to deal with environmental change and human disturbance. To assess the effectiveness of ecological construction and analyze the influencing factors of ecosystem changes in the Qinghai-Tibet Plateau (QTP), this study detected the spatial changes and dynamic hotspots of vegetation ecosystems in the ecological construction regions of the QTP (QTPE) and regions without ecological construction (QTPWE) using hot spot analysis and comprehensive dynamic degree model. Then the random forest (RF) model and geographical weighted regression model were used to study the degree and spatial heterogeneity of impacts of climate and human activities on normalized difference vegetation index (NDVI). Results showed that the vegetation restoration of the QTPE was obvious during 2001-2018 as the area of the increasing NDVI accounted for 74.15%. In addition, the effects of climate and human activities on NDVI of vegetation ecosystem showed significant spatial heterogeneity. The RF model showed that population density was the most significant factor affecting ecosystem vegetation in the QTPE, and its relative importance was between 26.1-32.6%, followed by downward shortwave radiation (7.9-16.8%). However, climate factors still had the greatest impact in the QTPWE, with the relative importance of precipitation and temperature being 45% and 15%, respectively. These findings provide a scientific basis for the restoration and management of vegetation on the QTP, and are of great significance for the deployment of future ecological engineering projects., 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 © 2022 Elsevier Ltd. All rights reserved.)

Under the "carbon peaking and carbon neutrality goals", China's commercial banks are facing a severe climate transition risk. This paper proposes a climate transition risk measurement method for commercial banks, and investigates the impact of climate transition risk on bank performance based on the data of 490 commercial banks in China from 2008 to 2019. The empirical findings are as follows: firstly, the climate transition risk has an inhibitory effect on the performance of commercial banks, and the inhibitory effect weakens with the increase of bank size. Secondly, the signing of the Paris Agreement and the increase of the economic policy uncertainty in China have a positive moderating effect, which weakens the inhibitory effect of the climate transition risk. Finally, the climate transition risk inhibits the performance of commercial banks partly by reducing the scale of bank loans., 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 © 2022 Elsevier Ltd. All rights reserved.)

With the intensification of climate warming and human activities, the ecosystems on the Qinghai-Tibet Plateau (QTP) are facing increasing threats which leads to extensive ecological degradation. Ecological restoration measures need to be implemented to improve biodiversity and ecosystem services to mitigate the impact of climate change and human disturbances. However, the key priority areas (KPAs) for ecological restoration are not clear on the QTP, and the benefits of ecosystem services for ecological restoration are often ignored. In this study, we are the first to identify the KPAs based on the quantitative evaluation method and multicriteria optimization algorithm under five restoration scenarios aiming at ecosystem service improvement on the QTP. Results showed that: (1) The benefits of ecological restoration for climate change mitigation and associated costs under different scenarios showed generally similar spatial variability, exhibiting higher in the south and lower in the west, which were different from those for biodiversity. (2) The restoration priorities in Sichuan and Yunnan were generally higher under scenarios Ⅱ and Ⅴ, while in Xinjiang, Sichuan, Yunnan, and western and southern Tibet were higher under scenarios Ⅰ, Ⅲ and Ⅳ. (3) For different ecosystems, the similarities lied in that the restoration priorities of wetland ecosystem were the highest, while those of desert ecosystem were the lowest under five restoration scenarios. (4) When the restoration area requirement was 25% of the total degraded area, the highest restoration priority levels under scenarios Ⅰ, Ⅲ and Ⅳ were mainly distributed in Guinan, Renbu, Nierong and Chayu counties, and under scenarios Ⅱ and Ⅴ were mainly distributed in Renbu, Lang and Guinan counties. When the restoration area requirements were 50% and 75% of the total degraded area, the counties with higher restoration priority levels under scenario Ⅱ were different from those under other four scenarios. This study identified the KPAs under different restoration scenarios, which provided references for the restoration measures implementation on the QTP., 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 © 2022 Elsevier Ltd. All rights reserved.)

Understanding the changes in hydrological process is a key subject for water resource management of a high-diversity watershed. In this paper, through an establishment of a SWAT-based model, the effects of climate change and its induced vegetation change on hydrological process were analyzed in the East River Basin. The model could well simulate the hydrological processes of the basin including surface runoff (SURQ), groundwater (GWQ), lateral flow (LATQ), total water yield (WYLD), actual evapotranspiration (ET), and groundwater recharge (PERC). Under the vegetation change induced by temperature increase, the effects of the vegetation change on hydrological process were larger than that of the temperature change. Under the vegetation change caused by the increase of temperature and precipitation, the vegetation change enhanced the effects of climate change on annual SURQ, LATQ, GWQ, WYLD, and PERC of the basin. Under spatial scale, when the temperature and precipitation changed simultaneously, the increase of precipitation could promote the increase of annual ET in sub-watersheds. Also, the annual SURQ, WYLD, GWQ and ET in western sub-watersheds were more sensitive to the cumulative changes of vegetation and climate. This work can provide useful information to decision makers in water resource management of watersheds., 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 © 2022 Elsevier Ltd. All rights reserved.)

As a major component of temperate steppes in the Eurasian continent, the Mongolian Plateau (MP) plays a pivotal role in the East Asian and global carbon cycles. This paper describes the use of five remote sensing indices derived from satellite data to characterize vegetation cover on MP, namely: gross primary production (GPP), net primary production (NPP), normalized difference vegetation index (NDVI), leaf area index (LAI) and fractional vegetation cover (FVC). It is found that GPP, NPP, and NDVI exhibit increasing trends, whereas LAI and FVC present decreasing trends on the MP since 1982. The different indices highlight discrepancies in the spatial pattern of vegetation growth, with the greatest increase in the southeast of MP. Only 3.4% of the total land area of MP exhibited consistent trends in the indices (0.1% degradation and 3.3% growth, P < 0.01), with the synchronous change of both LAI and NPP exhibiting higher consistency than that of raw NDVI and NPP. Understanding of the characteristics and status of vegetation change on the MP has far-reaching implications for its ecological protection management, and climate change mitigation., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Widespread concern about ecological degradation has prompted development of concepts and exploration of methods to quantify ecological quality with the aim of measuring ecosystem changes to contribute to future policy-making. This paper proposes a conceptual framework for ecological quality measurement based on current ecosystem functions and biodiverse habitat, compared with pixel-scale historical baselines. The framework was applied to evaluate the changes and driving factors of ecological quality for Chinese terrestrial ecosystems through remote sensing-based and ecosystem process modeled data at 1 km spatial resolution from 2000 to 2018. The results demonstrated the ecological quality index (EQI) had a very different spatial pattern based upon vegetation distribution. An upward trend in EQI was found over most areas, and variability of 46.95% in EQI can be explained well by change in climate, with an additional 10.64% explained by changing human activities, quantified by population density. This study demonstrated a practical and objective approach for quantifying and assessing ecological quality, which has application potential in ecosystem assessments on scales from local to region and nation, yet would provide a new scientific concept and paradigm for macro ecosystems management and decision-making by governments., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

While the Global North is historically responsible for the majority of greenhouse gas emissions in the atmosphere, Newly Industrializing Countries (NICs) are expected to overtake developed country emissions in the coming years. At the same time, NICs are climbing the ladder of the global economy, increasing their competitiveness on the global stage and catching up with technological competencies of developed economies. Against this background, this paper explores innovation and collaboration in Climate Change Mitigation Technologies (CCMTs) in NICs. The research question is whether the propensity to innovate and diffuse CCMTs is impacted by technological collaboration with two highly developed countries, Germany and The United States. The sample of NICs includes the BRICS (Brazil, Russia, India, China, South Africa) plus Israel, Mexico, and Turkey, in a panel from 1995 to 2015. The empirical results suggest that collaboration with both Germany and the U.S. is highly significant for domestic CCMT innovation in NICs. These findings are important because, stepping beyond the literature on the merits and drawbacks of global climate governance tools such as the Clean Development Mechanism (CDM) and related UNFCCC processes, they show that collaboration for climate and environmental technologies could become a key tool to significantly improve the chances to stay in line with the Paris Climate Agreement. Finally, the policy advice for NICs and developing countries is to, above all else, focus on incubating strong technological innovation systems, including strengthening domestic Intellectual Property Rights (IPR), as well as to enhance technological collaboration with developed countries., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Faced with severe and growing social inequality among elderly individuals, a more complete understanding of the interconnection between the seasonal migration of the elderly and climate change is clearly needed. This paper tries to clarify the relation between the seasonal migration of the elderly and climate change from a multidimensional perspective. Our study adds to the research on population aging against the background of climate change. This study shows that climate change has a significant impact on regional population aging and that there are significant internal differences between the distribution and scale of the migratory elderly stemming from unequal environmental conditions. Advanced economic conditions and relatively comfortable environments have put some pressure on social endowment services in the three provinces of Northeast China, especially Liaoning Province. With their complex migration mechanism, the fate of the elderly in mainland China is closely intertwined with climatic factors, individual and socioeconomic conditions, traditional cultural backgrounds, etc. A comparatively advanced economy, well-developed endowment resources, and a comfortable environment will help in the development of regional aging resources. Regional inequality and the pursuit of a meaningful life have created different categories of migratory elderly., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

This paper explores the historical relationship between weather fluctuations and mortality evolution in China. Using panel data on the annual provincial mortality rate and daily weather variables for the 1964-2008 period, we applied a dynamic model structure with sufficient controls to estimate the short-term effects of temperature shocks on China's mortality rate. Our main results show that an additional 1 °C rise in average temperature in a given year increases the annual mortality rate by an average of 3.2%. In addition, days with daily average temperatures exceeding 27 °C (30 °C) are associated with an increase in the annual mortality rate of nearly 0.1% (0.2%) or approximately 9,520 (19,040) additional deaths. In contrast, extremely cold days do not have these effects. By applying a hybrid model structure (the long difference approach) to examine the medium-term effects of temperature changes, we find that there is little evidence of overall adaptation to high temperatures when moving from the short term to the medium term. In addition, by introducing interactions between temperatures and potential modifiers such as access to doctors, hospital beds, air conditioning and refrigerators, we determined that only residential air conditioning played an important role in mitigating the temperature-mortality relationship. However, the penetration of air conditioning in China is still relatively low. These findings indicate that climate change could partially explain the historical phenomenon of China's rising mortality rate during the unprecedented economic boom experienced since the 1980s., Competing Interests: Declaration of competing interest The authors declared that they have no conflicts of interest to this work. We declare that we do not have any commercial or associative interest that represents a conflict of interest in connection with the work submitted., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

Globally, some vegetation has grown significantly over the past decades, but the climate benefits remain unclear, especially in the temperate regions. Understanding the biophysical effects and identifying the potential of vegetation will help to mitigate climate change. Here, we propose a vegetation-adjusted temperature index to understand how terrestrial vegetation growth in China affects the air temperature for 2001-2013, based on satellite-derived normalized difference vegetation index, near-surface air temperature (T a ) and the land surface temperature. Grassland growth and cropland growth are found to cool the T a by -0.08 ± 0.32 °C (mean ± one standard deviation) and -0.06 ± 0.28 °C, respectively. Forest growth results in net climate warming by 0.05 ± 0.29 °C. Biophysical effects, elevation and background climate are used to explain the climate impacts of vegetation. Results show that the biophysical effects dominate the climate impact. More specifically, evapotranspiration (ET) controls the daytime climate impact, and at night, an indirect effect of albedo (the release of daytime heat storage) dominates it. Lower precipitation, temperature and elevation reinforce the warming potential. Moreover, the effects of albedo and ET on climate are nonlinear. During the day, although lower albedo absorbs more incoming radiation, it releases more extra heat per unit ET that can compensate for the increased incoming radiation. At night, the warming effect reflects the release of daytime heat storage. Overall, tropical vegetation growth (-0.04 ± 0.10 °C) and warm temperate vegetation growth (-0.08 ± 0.15 °C) achieve the climatic benefits. Overall, the method proposed contributes to quantitatively evaluate the role of afforestation plan on regional climate cooling, and provides some policy/practical implications for future afforestation projects. Future afforestation projects should carefully consider the biophysical process and background climate to mitigate climate change., 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 © 2020 Elsevier Ltd. All rights reserved.)

Robustness and cost effectiveness are major concerns for sustainable stormwater management under deep uncertainty of climate change. Given that many traditional static planning strategies are not working with unpredictable future conditions, the possibility of system failure, and the lock-in effects, the Adaptation Pathway (AP) approach was adopted for dynamically robust and cost-effective planning in this paper. In order to increase optimization accuracy of multi-staged planning, a continuous definition of the AP optimization problem was raised by improving the simplified versions in existing studies. A case study in Suzhou, a provincial pilot Sponge City in China undergoing increasing annual rainfall and severe water environment deterioration, was included by integrating Long-Term Hydrologic Impact Assessment-Low Impact Development model with optimization methods, aiming to persistently control the non-point source total phosphorus loading below an acceptable amount in the following unforeseen 20 years via multi-staged low-impact development (LID) construction. A novel optimization method developed by the authors in a companion paper, namely marginal-cost-based greedy strategy (MCGS), was successfully applied to efficiently solve the continuous version of the AP optimization problem. The popular genetic algorithm (GA) was used as a contrast. A weather generator was elaborated based on four Representative Concentration Pathway scenarios and 17 spatial downscaled general circulation models to simulate the unforeseen future annual rainfalls that helped with evaluating cost effectiveness of each prospective LID plan. Results showed that the adaptation pathways optimized by MCGS could save the whole life net present cost of an LID plan by 1%-60% compared with those optimized by GA, and the computational efficiency of MCGS was over 13 times faster than GA., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

In the global context, countries must reduce carbon dioxide emissions to "net zero" by 2050 to limit global warming to 1.5 °C above pre-industrial levels. China's Nationally Determined Contributions (NDCs) are to peak carbon dioxide emissions by 2030 and achieve the carbon neutrality target by 2060. To monitor the achievements of the NDCs, establishing an annual-based accounting mechanism is necessary to record the carbon stocks in China, especially for Blue Carbon. The paper aims to present China's first national-scale systematic Blue Carbon accounting for mangroves, salt marshes, and seagrasses, covering the accounting subjects of physical and monetary assets, as well as relevant indicators for investors. It focuses on the design of the accounting method and system, the selection of data categories applied to this system, and the application of China's Blue Carbon accounting based on this accounting system. Taking Blue Carbon accounting as a starting point, this paper analyses the restoration prospects of these ecosystems and their application potential for NDCs in China compared with the terrestrial ecosystems. The results indicate that mangroves are the most cost-effective type of ecosystem, even compared to terrestrial forests. Thus, the paper provides policymakers with a new perspective on the decision-making of carbon sequestration relevant decisions, aiming to promote the monitoring, restoration, and expansion of China's coastal blue carbon ecosystem through the establishment of a sound Blue Carbon accounting system, and to help achieve the carbon neutrality goal in China's NDCs through regular and systematic monitoring of its national Blue Carbon inventories. • China's coastal blue carbon ecosystems are carbon sources rather than carbon sinks. • Mangroves are the most cost-effective coastal blue carbon ecosystem compared to salt marshes and seagrass beds. • The Blue Carbon of mangroves is more cost-effective than the Green Carbon of terrestrial forests when meeting China's Nationally Determined Contributions target of increasing forest stocks. • Establishing guidelines for Blue Carbon data requirements will lay a scientific foundation for formulating strategies and policies to cope with global climate change. [ABSTRACT FROM AUTHOR]

Renewable energy is not only an efficient way to ensure energy independence and security but also supports the transition to a low carbon economy and society. The progress of renewable energy technological innovation is an important factor that influences the development of renewable energy. An in-depth analysis of the driving factors that influence this progress is crucial to China's energy transition. Based on Chinese provincial data over 2000–2015 and panel data models, this paper regards the CO 2 emissions as climate change and explores the response of renewable energy technological innovation to intensive CO 2 emissions. We also analyze the effect of the driving factors such as energy price and R&D investment on this innovation process. The main conclusions drawn are: (1) There are significant differences in technological innovation levels across China's provinces. (2) We observe that the intensive CO 2 emissions have promoted renewable energy technological innovation level, meaning that innovation process responds actively to climate changes. (3) R&D investment from government and enterprise both are conducive for promoting the innovation level. (4) Energy price has an insignificant effect on innovation in renewable energy technologies and we attribute this to the unreasonable energy price mechanism. This paper provides clear evidence for understanding the role of innovation on climate change. • The drivers of the renewable energy technological innovation are explored. • There are significant differences in technological innovation across provinces. • Renewable energy technological innovation responds actively to climate changes. • R&D investment is conducive for promoting the technological innovation levels. • The government should give full play to the role of the energy price mechanism. [ABSTRACT FROM AUTHOR]

With the aggravation of global warming and the increasing demand for energy, the development of renewable energy is imminent. Floating photovoltaic (FPV) is a new form of renewable energy generation. However, the impact of FPV on the aquatic environment is still unclear. By long-term empirical monitoring and data analysis, this paper reveals the shading effect of large-scale FPV power station on aquatic environment for the first time. The results show that: (1) Compared with the non-photovoltaic (NP) zone, FPV only significantly reduces the concentration of dissolved oxygen in the photovoltaic (P) zone. (2) The concentration of chlorophyll a , nitrate nitrogen and total phosphorus increase, while pH and ammonia nitrogen decrease. FPV only causes an effect of the same order of magnitude as the initial concentration, and has no significant adverse effects on the nutritional status of the water body at a coverage ratio less than 50%. (3) FPV has a cooling effect on the water body during the daytime and a thermal insulation effect at night, with the most pronounced impact on peak water temperature (T w). The heating and cooling process of T w in P zone usually lags behind the NP zone by 1–3 h. The diurnal fluctuation and vertical difference of T w as well as the stability of water body are reduced under the shading of FPV, alleviating the influence of climate change on T w and water body stratification. (4) If 10% of the water area larger than 1 km2 in China are used to develop FPV, more than 900 million tons of CO 2 emissions can be reduced, and about 5 billion m3 water can be saved, which is significant in the context of climate change. In general, this paper provides a reference for the future aquatic environmental impact assessment of FPV and the formulation of related policies. • The impact of FPV on aquatic environment has been assessed. • The scale effect of FPV and impact of "fish-photovoltaic integration" are revealed. • Spatial-temporal and object specificity of impact on aquatic environment is reviewed. • The responds of FPV to the challenges of global climate change are further discussed. [ABSTRACT FROM AUTHOR]

Global warming has accentuated the effects of extreme heat on health. Health insurance, functioning as a risk management tool, has the potential to alleviate these impacts. Consequently, this paper investigates the correlation between extreme heat events and the demand for health insurance in China. Using data from the China Health and Nutrition Survey, we have observed a substantial increase in the likelihood of residents purchasing health insurance during extreme heat events. To be specific, for every extra day of extreme heat events annually, there is a 0.3% increase in the probability of purchasing health insurance. This effect is not uniform across different demographic groups. It is particularly pronounced among middle-aged and elderly individuals, rural residents, those with lower educational levels, higher income brackets, and individuals residing in underprivileged areas with limited access to green spaces and healthcare facilities. Furthermore, our study indicates that the increased frequency of extreme heat events not only impacts individuals' physical health but also triggers negative emotions, which in turn drive risk-averse behavior related to health insurance purchases. These findings carry substantial policy implications for mitigating the economic consequences of climate change. • Extreme heat increases the likelihood of residents purchasing health insurance. • Each additional day of extreme heat boosts health insurance purchases by 0.3%. • This effect varies among different populations. • Mechanistic analyses indicate extreme heat impacting physical and mental health. [ABSTRACT FROM AUTHOR]

In recent years, China has achieved numerous economic miracles but it has also been plagued by severe air pollution. The frequent hazy weather has severely restricted China's sustainable development. To investigate the nonlinear threshold effect of socio-economic factors on urban haze in China, this study constructs a spatial econometric Smooth Transition Autoregressive Regression (STAR) model based on the STIRPAT theory by using the remote sensing inversion PM 2.5 data of 223 prefecture-level and above cities in China mainland during 2004–2016. In this study, the ARAR-STAR model is estimated by quasi-maximum likelihood estimation, and the accuracy of parameter estimation is verified by Monte Carlo simulation, which proves that the ARAR-STAR model constructed in this study is robust. It is concluded that: there is a complex spatial nonlinear relationship between socio-economic factors such as economic development level, population density, advanced industrial structure, energy consumption, opening-up, and haze pollution. The effect of socio-economic factors on haze emission reduction under the spatial influence has complex heterogeneity with the smooth transition between high and low regimes with economic development. The ARAR-STAR model constructed in this paper, which has both individual fixed effects and time fixed effects, expands the form of existing spatial panel nonlinear models and enriches and implements the application of spatial panel smooth transfer threshold models in the environmental field. Not only can it provide policy recommendations for China to achieve "coordinated efficiency in pollution reduction and carbon reduction" as soon as possible, but it also contributes to China's plan to address global climate change and promote global sustainable development. • Using spatial panel smooth transition autoregressive regression model for 2004–2016, we revisit the nonlinear effects of socio-economic factors on urban haze in China. • The ARAR-STAR model is estimated by quasi-maximum likelihood estimation. • We employ Monte Carlo simulation to obtain robust results. • The effect of population density, industrial structure, energy consumption, opening-up on haze emission has heterogeneity with the smooth transition between high and low regimes with economic development. [ABSTRACT FROM AUTHOR]