Search

Carbon dioxide capture, utilization, and storage (CCUS) technology is an emerging technology with large-scale emission reduction potential and an essential component of the global response to climate change to achieve net-zero goals. As the two most important countries in global climate governance, it is necessary to review and examine the current status and trends of research in the field of CCUS in China and the USA. This paper uses bibliometric tools to review and analyze peer-reviewed articles in the Web of Science from both countries during 2000–2022. The results show a significant increase in research interest among scholars from both countries. The number of publications in the CCUS field in China and the USA was 1196 and 1302, respectively, showing an increasing trend. China and the USA have become the most influential countries in CCUS. And the USA has a more significant academic influence on a global scale. Furthermore, the research hotspots in the field of CCUS are diverse and differentiated. That is, China and the USA pay attention to different research hotspots or have different focuses in different periods. This paper also finds that new capture materials and technology development, geological storage monitoring and early warning, CO2 utilization and new energy development, sustainable business models, incentive policies and measures, and public awareness are critical directions for future research in the field of CCUS, to provide a comprehensive review and comparison of CCUS technology development in China and the USA. It helps to gain insight into the research differences and linkages between the two countries in the field of CCUS and identify the research gaps between them. And place some consensus that policymakers can use. [ABSTRACT FROM AUTHOR]

The boom of building various industrial agglomerations in China has continued for many years. The relationship between industrial agglomeration and environmental pollution is the core issue of this paper. At the same time, green finance is a new financial development model that takes environmental protection as the core, and effectively promotes the sustainable development of the environment, the economy, and society while maintaining its own sustainable development, so green finance is a major direction for future financial development. This paper selects the data of 30 Chinese provinces from 2004 to 2020 as the research object and uses a system-GMM method to verify the relationship between industrial agglomerations, green finance on regional environmental pollution. It provides theoretical reference for improving global climate change and reduces environmental pollution (EP), concludes as follows. (1) Industrial agglomeration can exacerbate EP level in China, and also exacerbates EP in the western region, but the effect of industrial agglomeration on EP in the eastern and central regions has been insignificant. (2) Green finance can improve environmental pollution. The intensity of EP in economically less developed regions is more sensitive to green finance policies. (3) The correlation effect of industrial agglomeration and green finance can improve environmental pollution, i.e., green finance has an important role in improving EP. However, there are significant differences in different regions. The interaction term between industrial agglomeration and green finance with a non-significant positive coefficient in the eastern region, and it is non-significant negative coefficient in the central region, and a significant negative coefficient in the western region. The work has important theoretical value for achieving the goal of regional sustainable development and green transformation. [ABSTRACT FROM AUTHOR]

Climate change is an important issue for cities today and in the future. At present, China has a large population and complex climate conditions, and cities are also vulnerable to the adverse effects of climate change (Tian, Environ Sustain Dev 6: 153-155 2020). Three-dimensional greening can not only improve the green space system of a city but also have a far-reaching impact on the ecology, image, and economic benefits of a city. Therefore, the study of urban three-dimensional greening is an effective means to deal with climate change strategies. By exploring the influence of traditional greening and three-dimensional greening on Local Climate in Beilin District of Xi'an, Shaanxi Province, the mechanism of three-dimensional greening on urban ecological environment was discussed, and the ecological theory, urban three-dimensional greening theory, and urban local climate zone (LCZ) were referred to. Based on the methods of national climate monitoring, ENVI-met simulation, and field independent measurement, this paper selected a research sample site in the east section of Jianshe Road, Beilin District, Xi'an City, Shaanxi Province, China, and applied ENVI-met software to simulate the thermal stress relationship among building exterior surfaces, plants, and air in the street; quantified the overall ecology of the area; and used measuring instruments. The influence of different types of greening in the base on the site temperature, humidity, CO2 (carbon dioxide) concentration, wind speed, and other climate factors data was, respectively, measured and analyzed. The grid analysis was used to compare the traditional greening and three-dimensional greening, then the numerical differences of each impact factor were sorted out, and the effect of three-dimensional greening on the improvement of urban ecological environment was discussed by analyzing the climate factors with greater impact. The results show that (1) three-dimensional greening plus traditional greening is the most beneficial mode; (2) in the same environment, according to the parameter of 1.5 m from the ground in the model environment, it can be seen that the temperature of the space treated with three-dimensional greening of buildings is reduced by 3.5–3.6 ℃ compared with the control group, the relative humidity is different by 7–8%, the CO2 concentration is reduced by about 5%, and the spatial wind speed is relatively small. (3) When the urban green coverage rate is more than 40%, the improvement of temperature is more obvious, if it reaches 50%, the cool phenomenon in summer can be fundamentally changed. From the perspective of human perception, the PMV index increased by 0.27 on average. This paper discusses and analyzes the three-dimensional greening of urban streets in Beilin District, Xi'an City, Shaanxi Province, China, and studies its influence on urban ecology to different degrees. The conclusions are as follows: Different types of greening have different degrees of influence on urban climate. Meanwhile, the experimental results of this paper show that in cities like Xi'an, Shaanxi Province, China, where summer is hot, adding three-dimensional greening to traditional street greening can significantly improve the environmental microclimate, which is an effective means to cope with climate change, improve the site environment, and stabilize the urban ecosystem. [ABSTRACT FROM AUTHOR]

Climate change has been a widely concerned issue for decades. As the key policy target, the economic impact caused by climate change has received general attention from scholars and governments around the world. For the number of literatures is huge and the relationships among the literatures are not clear, we aim to clarify the research hotpots and the research trends of current literatures and provide inspiration for the development directions of future research in this paper. Using the bibliometric method, this paper characterizes the literatures on the economic impact of climate change based on the Web of Science (WoS) Core Collection. The results reveal that the USA occupies the leading position of the studies, which publishes most documents, and contains the most productive institutes and well-known scholars. From 2009, the number of documents published by a Chinese scholar started to increase rapidly, which makes China the second most productive country in recent years. The journals both belong to the WoS Categories of economics and environmental sciences and tend to publish more literatures than others. Adaptation, vulnerability, uncertainty, economic growth, climate policy, ecosystem service, energy consumption, renewable energy, food security, and land use are the representative keywords that have both high frequency and high centrality. Potential benefits, fat-tailed risk, social cost, international migration, and sustainable intensification are the top five main research hotspots. Based on the citation network of the top 50 documents with the highest local citation score, four research trends are sorted out: (i) the methodological innovation to monetized estimate the economic impact of climate change, (ii) the effect of current and future adaptive measures on agriculture, (iii) the interactional relationship between induced technological change and carbon tax, and (iv) the effect on labor market caused by climate change. [ABSTRACT FROM AUTHOR]

The world is facing the challenges of climate change and energy structure adjustments. The role of digital finance, a new branch of business that combines digital technology and traditional financial products, in reducing global carbon emissions needs to be studied. This paper uses panel data on 280 cities in China from 2011 to 2019 to empirically examine the efficacy of digital finance for governing carbon emission reductions and the mechanism by which it does so. The results show that (1) digital finance can facilitate carbon emission reductions and help reduce carbon emission intensity within regions; (2) digital finance helps promote the rational allocation of resources and alleviates factor distortions by encouraging firms to rationally use their own factor endowments so as to reduce carbon emission intensity, which holds robustly after considering the endogenous issues such as possibly omitting variables and collinearity; and (3) differences in geographical location, the vitality of regional innovation and entrepreneurship, regional willingness to protect the environment, and environmental protection levels lead to heterogeneity in the effect of digital finance on carbon emission intensity. Therefore, it is necessary to vigorously develop digital finance as a long-term tool for carbon governance. [ABSTRACT FROM AUTHOR]

Based on the annual mean PM2.5 concentration data of 30 provinces and cities in China from 2006 to 2020, this paper constructs a structural model of manufacturing haze pollution, expounds the main mechanisms affecting haze pollution, then statistically decomposes the main factors affecting the change of pollution emissions, and examines the direct and indirect impact paths of the change of haze pollution emissions in China. The study found that (1) the inhibitory effects of latent variables on manufacturing haze pollution is from strong to weak in order: industrial structure upgrading, environmental regulation, trade opening, productivity improvement, and economic scale expansion, while the optimal path for the indirect effect of exogenous latent variables on haze pollution is to rely on environmental regulation and opening up to achieve the improvement of productivity, so as to achieve the purpose of reducing haze pollution. (2) The analysis based on the PVAR model found that industrial structure adjustment is the key factor of haze decline in both the short and long term. Environmental regulation has an obvious effect on haze control in the short term, but in the long run, it still needs to rely on industrial structure adjustment, production efficiency improvement, and trade opening to achieve the goal of reducing haze. (3) There is an inverted U-shaped nonlinear relationship between output scale and environmental regulation. In addition, trade openness has a long-term effect on productivity. Technology spillovers from opening up can reduce haze pollution in the long run by improving productivity. (4) The environmental regulation policy in the central region is characterized by "race to the bottom," while the eastern region shows the characteristic of "race to the top" in the policy game of improving productivity and optimizing the industrial structure. Therefore, haze control requires an appropriate intensity of environmental regulation to reduce the proportion of high pollution and high energy consumption industries. Make full use of the international cooperation platform of the "the Belt and Road Initiative" and the pilot free trade zone to promote substantive cooperation between Chinese enterprises and developed countries in the field of environmental protection technology, increase investment in research and development of clean equipment and cleaner production technology, improve productivity, help China's green manufacturing, and contribute Chinese wisdom and Chinese solutions to global environmental governance and climate change. [ABSTRACT FROM AUTHOR]

Climate change has a profound impact on human survival and development. Climate change is an energy and economic issue, which should be driven by technology. Total factor energy efficiency (TFEE) improvement is undoubtedly a breakthrough in solving energy problems. In this paper, the fixed effect model, impulse response function, and threshold regression model are used to test the complex relationship between export sophistication and TFEE. The results reveal that export sophistication improvement leads to higher TFEE. The impulse response results verify the existence of a phased and positive correlation between export sophistication and TFEE. Finally, the relationship between export sophistication and TFEE significantly varied across different threshold levels of regional corruption score, economic development, and openness. This article led the foundation for supporting China's export sophistication promotion strategy and also provides a reference for energy efficiency improvement and energy crisis response in the post-pandemic era. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to strengthen research concerning the impact of climate change on China's water resources, in order to avoid extreme weather events such as regional rainstorms and floods affecting the stable supply of energy and electricity. The goal is to reorganize energy industry allocation to make China's energy the most efficient, which would thus help achieve sustainable development goals. The biggest contribution and of this paper is not only to enlarge the scale and take into account the sustainable aspects, but also to consider climate change factors that have been ignored in the past as exogenous variables. For the first time, we take stock of the optimal allocation of new and tradition energy sources and carbon dioxide. This study uses the Dynamic ZSG-DDF model with exogenous variables and undesirable outputs to find a way to redistribute carbon dioxide, new energy, and tradition energy from different provinces on the premise of maximizing efficiency, thus achieving the goal of reducing carbon and saving the earth. The research results show that the energy efficiency of tradition energy is higher than that of new energy in various provinces. At the same time, the China government must comprehensively review the quota targets of new energy, tradition energy, and carbon dioxide (CO2) in each province as quickly as possible. Currently, the quotas of various provinces are seriously misplaced, and 26 provinces in total have great room for improvement. [ABSTRACT FROM AUTHOR]

As the primary source of carbon dioxide fixation, vegetation is critical to the carbon sink process. In this paper, the Net Primary Productivity (NPP) and the Gross Primary Productivity (GPP) were simulated using the Carnegie-Ames-Stanford Approach (CASA) model and the Vegetation Photosynthesis Model (VPM), respectively, and then the Potential Gross Primary Productivity (PGPP) and the GPP affected by human activities (AGPP) were simulated by combining Potential Net Primary Productivity (PNPP), and then the impact of climate change and human activities on GPP was assessed in the Heihe River Basin (HRB). The results showed that the GPP of grassland and Bare or Sparse Vegetation (BSV) exhibited a fluctuation rise, with increases of 0.709 gCm−2 a−1 and 0.115 gCm−2 a−1, respectively, whereas the GPP of cropland showed a fluctuation reduction, with a decline rate of −0.465 gCm−2 a−1. Climate change and human activity are both positive for vegetation growth, and human activity being the primary factor influencing GPP change. Human-dominated vegetation restoration accounted for 56.1% of the overall restoration area, with grassland GPP being the most visible response to human activities. The GPP changes in crop and grassland had a positive correlation with precipitation but a negative correlation with temperature among climate change factors, whereas the GPP changes in BSV had a negative correlation with both precipitation and temperature. Quantitative analyses of climate change and human activities' dynamic contributions to vegetation can give scientific and theoretical insight for dealing with global climate change. [ABSTRACT FROM AUTHOR]

This paper examines the relationship between climate change and the spatial distribution of population in China. We establish a two-way fixed effects model to investigate the role of temperature difference in the spatial distribution of China's population. We find that the annual variation of temperature has an impact on city size in both large and small cities, and that city size tends to shrink as the temperature difference increases. Meanwhile, we also find that the population in the cities located south of Qinling-Huaihe Line and Aihui-Tengchong Line (Hu's Line) is more sensitive to temperature effects, and that the temperature difference has a significant negative effect on city size. Similarly, the same results are found for prefecture-level cities with low administrative levels. Considering the endogeneity between temperature change and city size, we adopt an instrumental variable using latitude to perform a more robust empirical analysis, the results of a series of robustness tests support these conclusions. [ABSTRACT FROM AUTHOR]

The problem of global climate change is a common problem faced by all countries, and developing countries will face heavier pressure to reduce carbon emissions while promoting economic development. China is currently in a period of economic transformation, and the contradiction of industrial structure upgrading is becoming more and more prominent. The unreasonable industrial structure has caused a large consumption of resources, hindering the optimal allocation of resources and affecting carbon emissions. Technological innovation is an important force to cope with environmental changes, promote economic growth, and improve comprehensive competitiveness. Can technological innovation achieve carbon emission reduction under industrial structure upgrading? In this paper, industrial structure upgrading, technological innovation, and carbon emissions are studied together, and the impact of industrial structure upgrading, technological innovation on carbon emissions is theoretically analyzed, as well as how industrial structure upgrading acts on carbon emissions through technological innovation. An empirical test is conducted using 30 provincial panel data in China from 2005 to 2017, and it is found that industrial structure upgrading can reduce carbon emissions. An econometric analysis based on the mediating effect model finds that industrial structure upgrading has both a direct carbon emission reduction effect and a mediating effect of reducing carbon emissions through technological innovation, and the mediating effect of technological innovation in reducing carbon emissions remains significant in the regression analysis of sub-regions. Based on the research of this paper, it is suggested that the advanced development of industrial structure should be promoted, the rationalization of industrial structure should be promoted, the innovation development strategy should be firmly implemented, and the level of technological innovation should be promoted. [ABSTRACT FROM AUTHOR]

Economic development has brought about global greenhouse gas emissions and, thus, global climate change, a common challenge worldwide and urgently needs to be addressed. Accurate carbon price forecasting plays a pivotal role in providing a reasonable basis for carbon pricing and ensuring the healthy development of carbon markets. Therefore, this paper proposes a two-stage interval-valued carbon price combination forecasting model based on bivariate empirical mode decomposition (BEMD) and error correction. In Stage I, the raw carbon price and multiple influencing factors are decomposed into several interval sub-modes by BEMD. Then, we select artificial intelligence-based multiple neural network methods such as IMLP, LSTM, GRU, and CNN to conduct combination forecasting for interval sub-modes. In Stage II, the error generated in Stage I is calculated, and LSTM is used to predict the error; then, the error forecasting result is added to the first stage result to obtain the error-corrected forecasting result. Taking the carbon trading prices of Hubei, Guangdong, and the national carbon market, China, as the research object, the empirical analysis proves that the combination forecasting of interval sub-modes of Stage I outperforms the single forecasting method. In addition, the error correction technique in Stage II can further improve the forecasting accuracy and stability, which is an effective model for interval-valued carbon price forecasting. This study can help policymakers formulate regulatory policies to reduce carbon emissions and help investors avoid risks. [ABSTRACT FROM AUTHOR]

The purpose of this article is to investigate the new driving forces behind China's green energy and further assess the impact of green energy on climate change. The existing literature has used linear methods to investigate green energy, ignoring the non-linear relationships between economic variables. The nonparametric models can accurately simulate nonlinear relationships between economic variables. This paper constructs a nonparametric additive model and uses it to explore green energy. The empirical results show that the impact of green finance on green energy is more prominent in the later stage (a U-shaped impact). Fiscal decentralization also exerts a positive U-shaped impact, meaning that expanding local fiscal autonomy has contributed to green energy growth in the later stage. Similarly, the impact of oil prices and foreign direct investment demonstrates a positive U-shaped pattern. However, the nonlinear impact of environmental pressure displays an inverted U-shaped pattern. Furthermore, this article explores the impact of green energy on climate change and its impact mechanisms. The results exhibit green energy generates a positive U-shaped impact on climate change, meaning that the role of green energy in mitigating climate change gradually becomes prominent over time. Mechanism analysis exhibits that industrial structure and energy structure both produce a nonlinear influence on climate change. [ABSTRACT FROM AUTHOR]

China is the largest carbon emitter in the world. Understanding carbon emissions of China, especially at the provincial level, will help identify the critical factors behind carbon emissions and effectively implement carbon emission reduction measures. There are significant achievements in the study of carbon emissions of China's provinces. However, there is a gap for improvement in the study from periods and groups' perspectives using a decomposition-clustering method. This paper adopts the Logarithmic Mean Divisia Index (LMDI) to decompose each province's carbon emissions, introduces the elbow and K-means methods to cluster provinces based on the driving factors of decomposition, and analyzes the driving factors of carbon emissions from the view of groups and periods. By analyzing the carbon emissions data of 28 provinces in China from 1998 to 2018, a breakthrough has been found that economic activities and energy intensity were the main driving factors of carbon emissions. Some possible countermeasures, such as optimizing the industrial structure and the energy structure, significantly increasing clean energy consumption, would receive effective carbon emission reduction feedback. The results provide better decision-making support for emission reduction policies in China and contribute to global climate change issues. [ABSTRACT FROM AUTHOR]

Blue-green algae (CyanoHABs), photosynthetic bacteria that create a harmful aquatic environment, have been a trending issue on Taihu for over a decade. CyanoHABs adapt to varying climatic changes, which explains why the problem on Taihu still thrives. One major drive that keeps the algae is Sediment Oxygen Demand (SOD). In this paper, seasonal and spatial variations of SOD that contribute immensely to nutrient growth in Lake Taihu were done using the Environmental Fluid Dynamics Code (EFDC). The results were analyzed based on Nitrogenous SOD (NSOD) and Total SOD (TSOD). Summer results ranged from − 0.05754 to − 0.0826 (− 0.75658 to − 0.83902) (g/m2/day) and Winter values ranged from − 0.3022 to − 0.40171 (− 1.34486 to − 1.48856) (g/m2/day) indicate a gradual decrease in NSOD (TSOD) values respectively. Relatively higher values in summer are attributed to warmer surface water which sets up thermal stratification to increase the internal loading of nitrogen. Lower winter values are related to inverse stratification, where lower oxygen concentration decreases the SOD to trigger ammonium accumulation in the water column. NSOD (TSOD) values for Autumn results ranged from − 0.1039 to − 0.24786 (− 0.96251 to − 1.39454) (g/m2/day) and Spring values of − 0.43019 to − 0.35959 (− 1.48297 to − 0.54089) (g/m2/day). Transition seasons (i.e., Autumn and Spring) results are impacted by wind mixing that allows dissolved oxygen and nutrients in the whole water column. However, spring values depict a gradual increase in SOD value attributed to spring turnover and gradual stratification, which decrease nutrient concentration. In contrast, decreasing SOD values in autumn are related to mixing, but temperature decreases tend to increase nutrient concentrations. Carbonaceous sediment oxygen demand (CSOD), due to sulfide oxidation, presents high values from the difference between TSOD and NSOD. Based on the high values of CSOD, it is highly recommended that more research on eutrophic Taihu lakes would consider delving into CSOD. [ABSTRACT FROM AUTHOR]

Financial activity plays an important role in the analysis of environmental and ecological implications. Previous research shows that shadow banking activities, informal and less regulated financial activities, have become an important part of financial systems after the 2008 financial crisis. However, the impact of shadow banking activities on carbon dioxide emissions has been ignored in previous research. In this paper, we use the panel annual data of 30 Chinese provinces over the period 2013–2020 to study the effect of shadow banking activities on carbon dioxide emissions. The result shows that shadow banking activities have a positive impact on carbon dioxide emissions, and the result is still robust by using dynamic panel regression with general method of moments (GMM) method. Heterogeneity analysis shows that the impact of shadow banking activities on carbon emissions is more pronounced for the secondary industry, which proves that shadow banking activities support more for environmentally unfriendly businesses. Furthermore, by using difference-in-difference (DiD) method, we examine the causal effect of shadow banking regulation policy on carbon dioxide emission reduction and demonstrate that the shadow banking regulation can reduce carbon dioxide emissions. Our research contributes to environment protection and achieving the goal of low-carbon economy by exploring the role of shadow banking activities and financial regulation in carbon dioxide reduction. [ABSTRACT FROM AUTHOR]

Increasing carbon productivity is an important measure taken by China to deal with global climate change, and technological innovation is the fundamental way to promote industrial carbon productivity. To explore the low-carbon effects of technological innovation, based on the panel data of 30 provinces in China from 2009 to 2017, this paper established a spatial panel measurement model and a panel threshold regression model to explore the spatial spillover effects and threshold characteristics of technological innovation on industrial carbon productivity. The research shows the following: on the one hand, technological innovation and industrial carbon productivity each has obvious spatial correlation, and technological innovation has a significant spatial spillover effect on the improvement of industrial carbon productivity, and the indirect spillover between regions is greater than the direct spillover effect within the area. On the other hand, the impact of technological innovation on industrial carbon productivity has a double threshold effect. With the continuous improvement of technological innovation capabilities, the promotion of industrial carbon productivity has become increasingly more influential. Through the division of threshold values, the technological innovation capabilities of various regions in China are significantly heterogeneous, and the overall level is low. Although technological innovation capabilities have improved in recent years, there is still much room for improvement. Finally, this article puts forward relevant suggestions from the construction of regional technological innovation system, economical green circular development, and the establishment of a green technological innovation system. [ABSTRACT FROM AUTHOR]

This paper introduces a novel methodology for estimating carbon total factor productivity based on the Cobb–Douglas production function. The research method introduced in this article expands upon the framework for assessing total factor productivity through the Solow residual method. It unifies the conceptual and methodological aspects of carbon total factor productivity with those of single factor productivity. Utilizing panel data from various provinces between 2010 and 2021, we computed carbon total factor productivity to understand its implications for China in combating global climate change. We demonstrate that (i) we have introduced a method to handle negative coefficients in the Cobb–Douglas production function by incorporating productivity in lieu of input factors during calculations. (ii) Carbon total factor productivity, encapsulating the geometric weighted mean of labor, capital, and carbon productivity, holds notable economic relevance. Further, it serves as an integrative metric comprising carbon productivity intertwined with the mean labor and capital carbon factors. And (iii) the influence of carbon total factor productivity growth on economic progression remains relatively subdued, with escalating labor force growth posing detrimental effects on several provincial economies. Enhancing carbon total factor productivity emerges as an imperative to harmonize robust economic growth with strategic carbon curtailment. Our analytical framework provides nuanced perspectives on productivity determinants, accentuating the thrust towards sustainable evolution amidst climatic challenges. This investigation bears profound significance for policymakers endeavoring to sculpt a carbon-conscious economic paradigm in consonance with global climatic ambitions. [ABSTRACT FROM AUTHOR]

The carbon mitigation response encompasses a variety of strategies aimed at mitigating greenhouse gas emissions resulting from human activities. These measures are crafted to address the challenges posed by climate change and facilitate the transition of businesses towards a low-carbon paradigm. Leveraging the analytical outcomes of the extended STIRPAT model and the PSO-BP prediction model, this paper suggests countermeasures for reducing carbon emissions in China's metal smelting industry. The overarching objective is to contribute to China's attainment of the "dual carbon objectives." The study identifies key factors influencing carbon emissions in the metal smelting industry, ranked in descending order of sensitivity: population, coal consumption, urbanization rate, total metal production, carbon intensity, proportion of secondary industry, and GDP per capita. Results from three established scenarios—namely, low carbon, standard, and high carbon—indicate a consistent decline in carbon emissions from China's metal smelting industry over the next 15 years. This research not only enhances the findings of existing studies on carbon emissions in the metal smelting sector but also introduces an innovative approach to carbon emission reduction within China's metal smelting industry. [ABSTRACT FROM AUTHOR]

To increase the degree of implementation of the rural revitalization strategy, it is crucial to conduct a scientific investigation of the relationship between fiscal policy, fiscal decentralization, green digital management, and contract selection. This research develops an analytical framework for the application of fiscal policy and the choice of cooperative association contracts based on the contract theory. On the basis of a theoretical analysis of the external factors that influence the choice of residents' cooperative association contract and behavior, it goes on to investigate the interest game relationship and stability of three different cooperative associations (production type, service type, and industrial chain type). Additionally, it runs an empirical test using survey information from cooperative associations in China's Gansu Province. The findings indicate that the cooperative association is more likely to select the factor contract model depending on the strength of the fiscal policy support, the union's brand influence, and the regional market share change of the primary products. Evolutionary equilibrium is inhibited by the association's input costs, but a stable contract is created more favorably the more average the cost distribution is. The stability of the contract and its detrimental effects on pro-environmental behavior are worse the more damage other residents' breaches of the contract have caused. The research findings in this paper may serve as a basis for decision-making and a source of information for encouraging the high-quality growth of cooperative groups. [ABSTRACT FROM AUTHOR]

Climate change is a challenge for all of humanity. Should financial be environmentally responsible? This paper uses the Toda–Yamamoto test and the extended Fourier Toda–Yamamoto test re-examine the relationship between financial development and environmental quality in 31 provinces and municipalities in China during the period 2000–2018. We find that financial development in certain regions has effectively reduced the concentration of PM2.5, which indicates that it has a significantly positive effect on environmental quality, though with regional differences. After considering structural shifts, the relationship between financial development and environmental improvement is found to be significant in more regions, indicating that China has undergone structural shifts in these aspects. The aforementioned conclusions are also supported by further robustness tests. China can consequently utilize the positive impact of finance for advocating carbon emission reduction and improving environment quality, therefore contributing to the response to global climate change. [ABSTRACT FROM AUTHOR]

Net primary production (NPP) is an essential component of the terrestrial carbon cycle and an essential factor of ecological processes. In global change research, it was the core content to study the driving forces of NPP change. In this paper, we focused on the Southwest Karst area of China and analyzed the response mechanisms of NPP to topography, land-use types, climatic change, and human activities. Our results showed that (1) changes in elevation and slope lead to significant differences in the spatial distribution of NPP. With the increase of elevation and slope, NPP first increased and then decreased, their critical values were 2000 m and 15°, respectively. (2) NPP varied significantly among different land-use types. The average NPP of the forest was the highest, and the average NPP of cultivated land increased fastest. (3) Temperature and precipitation had the most substantial influence on NPP, both of them promoted the increase of NPP, and the effect of temperature was more obvious in the Southwest Karst area. (4) Ecological engineering significantly promoted the change of NPP, while animal husbandry significantly inhibited the change of NPP. (5) There were significant spatial differences in the driving effects and corresponding contributions of climatic change and human activities; both of them promoted the increase of NPP in the Southwest Karst area of China. Under climatic change and human activities, NPP increased by 1.24 gC·m−2·year−1 and 2.29 gC·m−2·year−1, respectively. The contributions rates of climatic change and human activities separately accounted for 35% and 65%. The contribution of human activities on NPP was much higher than that of climatic change in the Southwest Karst area, and the results suggested that we should focus on the role of human activities on NPP change. [ABSTRACT FROM AUTHOR]

With the statement of Chinese government on energy saving in 2020 at the United Nations General Assembly, carbon neutral was widely spread as a new concept. As a big country, China has the responsibility and obligation to make its own contribution to global climate change. This paper aims to explore and find effective ways for China to achieve carbon neutrality by 2060. We identify the main factors affecting carbon emissions by STIRPAT model; combined with the scenario analysis, we divide the year 2020 to 2060 into three stages: year 2020–2030 is carbon peak stage, year 2030–2050 is rapid emission reduction stage, and year 2050–2060 is complete carbon neutralization stage. At each stage, three development models, high, medium, and low level, were established. There are a total of 27 different scenarios in three stages. A system dynamics model was established to simulate the effects of carbon emission factors and changes in carbon sinks in different scenarios. Finally, 8 paths were found which in line with Chinese current goal of achieving carbon neutrality with treating reach carbon peak in 2030 as an additional filter condition. Comparing per capita GDP levels in different scenarios, we eventually find that keeping economic development at a low level in the first stage, a high level in the second stage, and a medium level in the finally stage, the point where net carbon emissions are less than zero for the first time will appear between year 2056 and 2057. By then, the per capita GDP will reach 144,500 yuan (based on year 2000), nearly four times 2000's. In all, these findings are helpful for policymakers to implement reasonable policies to achieve carbon emission peaking and carbon neutral in China. [ABSTRACT FROM AUTHOR]

In recent years, global climate change caused by carbon dioxide emissions has attracted more and more attention. Adjusting the energy mix by predicting energy demands is currently a more effective way to address climate issues and energy supply issues. Based on the panel data from 1999 to 2018 in China, this paper designed a new hybrid prediction model to predict the future electricity consumption of Chinese residents by double improvement of particle swarm optimization. By comparing with the BP neural prediction model without mixing and several BP neural prediction models with other improved and mixed forms, the results show that the BP neural network hybrid prediction model with DPSO-BP is more suitable for forecasting the electricity consumption of Chinese residents. At the same time, the prediction results of the DPSO-BP prediction model show that the annual electricity consumption of Chinese residents will increase from 9685 (100 million kWh) in 2018 to 13,171 (100 million kWh) in 2025 in the next 7 years. The research results provide a reference for future scholars in the design of algorithms and provide suggestions for the government to adjust energy and avoid severe power shortages or surpluses. [ABSTRACT FROM AUTHOR]

Green credit encompasses financial instruments and services utilized to mitigate greenhouse gas emissions and facilitate adaptation to global climate change. Establishing a long-term stable green credit institution is crucial to promoting carbon abatement goals. This study uses the difference-in-difference (DID) model to discuss the impact of green credit policy (GCP) on environmental performance based on the China industrial enterprise data. Our results show that GCP inhibits the pollution emissions and improve firm environmental performance. This improvement effect is attributed to a reduction in production scale, and financing constraints. Moreover, GCP increases the firms' exit risk from market and promotes the technological innovation of incumbent firms. Economic growth target constraints trigger a positive moderation role in the implementation of GCP. Heterogeneity results show that such improvement effect is more pronounced in state-owned firm, large-scale firms, and high R&D intensity firms. Importantly, our findings also suggest the environmental monitoring effect of green credit is dependent on the institutional quality. Only in a sound market environment can GCP effectively improve firm environmental performance. Finally, we propose to build a systematic incentives and constraints mechanism to achieve the sustainable development. The conclusions of this paper provide empirical evidence and policy implications for the implementation of GCP. [ABSTRACT FROM AUTHOR]

The increased growth of vegetation has the potential to slow global climate warming. Therefore, analyzing and predicting the response assessment of Chinese vegetation to climate change is of great significance to studies of global warming. In this paper, we examine the spatiotemporal dynamics of vegetation leaf area index (LAI) values in China from 1981 to 2017 and their correlations with meteorological (hydrothermal) factors based on trend analysis and correlation analysis. We further construct an LAI prediction model based on hydrothermal conditions. The climate data obtained under different scenarios in the CMIP5 and CMIP6 climate models were used to predict the dynamic change trend of vegetation LAI from 2021 to 2100. The results show that most areas of China (72.82%) showed an improving trend in vegetation LAI from 1981 to 2017, during which the annual average LAI value increased at a rate of 0.0029 year−1. Vegetation LAI in China was significantly correlated with climatic factors (temperature, precipitation, and evapotranspiration), and the LAI prediction model constructed based on hydrothermal conditions had a high accuracy (Pearson's Cor value is 0.9729). From 2021 to 2100, approximately 2/3 of China's vegetation LAI area showed an improvement trend, and the impact of climate change on vegetation LAI predictions under the high emission scenario was greater than that under the low emission scenario. This research can provide a basis for studies on the climatic drivers of vegetation change and the global vegetation dynamic model. [ABSTRACT FROM AUTHOR]

In response to global climate change, the Chinese Government has taken numerous measures to promote low-carbon management practices, but the overall adoption rate has been lower than expected. Empirical studies on the path dependence of farming experience, that is, long-standing planting concepts that will hinder farmers from adopting new technologies, have not been reported. Hence, to fill the research gaps, this paper uses survey data from 805 rice farmers in Zhejiang, Hubei, and Jiangxi provinces, China, to examine the impact of farming experience on the adoption of soil testing and fertilizer recommendations. The results show that farming experience significantly negatively affects the adoption of low-carbon practices, especially among farmers with low resource endowment. However, farmers, who make decisions based jointly on farming experience and social networks, are more likely to adopt low-carbon practices. This means that as long as farming experience is used reasonably, for example, by broadening the social network of farmers and urging them to form a decision-making method that comprehensively utilizes farming experience and social networks, it can also demonstrate value. Our findings contribute meaningfully to the development of efforts to promote the adoption of low-carbon management practices in China. [ABSTRACT FROM AUTHOR]

The Chinese government has established carbon emissions trading schemes (CETS) to cope with climate change and reduce carbon emissions. This paper uses the multi-period difference in difference (DID) model to explore the impact of CETS on cities' GDP and GDP growth rates in China based on data from 285 prefecture-level cities from 2008–2019. We find that CETS increase the level of GDP by 3.4% and the GDP growth rate by 0.004. The mechanism of increasing GDP includes promoting investment and innovation, and the mechanism of increasing the GDP growth rate includes only promoting the investment growth rate. Thus, CETS satisfy both the weak and strong versions of the Porter Hypothesis from the GDP and growth rate perspectives. In the heterogeneity analysis, we find that the impacts of CETS on cities' GDP and GDP growth rates decrease with the increase of the corresponding dependent variable. In the spatial spillover analysis, we find that the establishment of CETS has a positive spillover effect on the GDP of cities around 50 km and 150 km, and a negative spillover effect on the GDP of cities around 200 km, but there is no spatial spillover effect on the GDP growth rate. [ABSTRACT FROM AUTHOR]

In the context of low-carbon economic development, carbon productivity has effectively integrated the two major objectives of carbon reduction and economic growth, and increasing carbon productivity has become the main approach to address global climate change. Using data from China's input–output table for 2002–2017, this study measures the evolutionary characteristics and influencing factors of carbon productivity from the perspective of embodied carbon emission in China's industrial sector. The results indicate that, first, China's industrial sectors' carbon productivity shows an increasing trend from 25.22 to 65.3 million yuan/10,000 tons of CO2 in 2002–2017, but the overall level is low. The energy efficiency factor is a major element affecting improvement in carbon productivity. Second, the carbon productivity of the 28 industrial sectors shows an increasing trend. There is a significant gap in the carbon productivity between different industrial sectors. The energy efficiency factor in all industrial sectors is positive, indicating that energy efficiency is a positive factor in increasing carbon productivity. Third, from largest to smallest in the carbon productivity are the primary, tertiary, and secondary industries. Finally, this paper recommends several approaches to improve carbon productivity from the perspectives of technology, differentiated policies of carbon emission reduction, and industrial structure adjustment. [ABSTRACT FROM AUTHOR]

We investigated the potential impacts of climate change on ecosystem services and their components in two distinct ecosystems: the northern grasslands and southern hills in China. The effects of minimum, average, and maximum temperature, and precipitation at monthly, seasonal, and yearly scales on ecosystem services and their components were studied through stepwise multiple regression analysis. The results showed that in the northern grasslands, an increase in the total ecosystem services value (ESV) was mainly attributed to soil conservation, biodiversity, hydrological regulation, and aesthetic landscape. In the southern hills, an increase in total ESV in each region was mainly attributed to climate regulation, environmental purification, biodiversity, and aesthetic landscape. There were strong correlations between ESVs and fluctuations in temperature and precipitation. In the northern grasslands, temperature was the main driving factor of the values from 11 categories of ecosystem services in Anxi, Tumuji, and Xilingol. However, in West Ordos, precipitation negatively affected the change in ESVs. In the southern hills, ESVs were governed by both precipitation and temperature in Huaying. Precipitation variables were an important factor affecting the ESVs in Cili. There was a stronger correlation between temperature and the majority of ESVs in Danjiangkou, Chongyi, and Lechang than precipitation. This paper provides a basis for a better understanding of the impact of climate change on different ecosystem services, and helps to enhance ESV under climate warming. [ABSTRACT FROM AUTHOR]

To cope with climate change, it is of great importance to describe the temporal and spatial evolution of climate change vulnerability and its driving factors. Therefore, this paper establishes a comprehensive index of vulnerability to climate change based on the vulnerability scoring diagram (VSD) framework. Moran's I index is used to study the spatial evolution characteristics of vulnerability, and spatial regression analysis is used to explore the factors influencing the spatial distribution of vulnerability. The results show that (1) the climate change vulnerability of China has decreased over time, and the sensitivity state is relatively stable; however, the annual change in exposure and adaptive capacity is significant. (2) The western region of China is more vulnerable than the eastern region, and the most vulnerable provinces are Guizhou and Gansu. (3) The regional vulnerability is generally in a significant spatial agglomeration state. (4) Finally, the driving factors of the spatial distribution of climate change vulnerability include forest coverage, the urban-rural income gap and information technology. These recommendations provide detailed discussions and scientific information for mitigating global warming and formulating long-term emission reduction targets, thereby optimizing resource allocation and providing spatial governance directions for the formulation of adaptation policies. [ABSTRACT FROM AUTHOR]

The global level of climate change agreement and the extensive development of China's industrialization process have caused China to face severe pressures regarding energy conservation and emission reduction. Tourism industry plays an important role in promoting steady economic growth and improving ecological environment in China. The agglomeration economic effect it produces can reduce carbon emissions, but the crowding effect may not be conducive to low carbon development. Therefore, how to reduce carbon emissions while promoting the development of tourism industry has become an urgent problem. This paper measures the level of tourism industry agglomeration and carbon emission in 30 regions of China and uses the method of spatial econometrics to analyze the impact of tourism industry agglomeration on carbon emissions. The empirical results prove that tourism industry agglomeration can reduce the carbon emissions of local and neighboring regions in China. However, this does not mean that the larger the scale of tourism industry agglomeration, the more conducive to the reduction of carbon emissions. For developed tourism groups, the impact of agglomeration effect on carbon emissions of local and adjacent areas shows a U-shaped relationship. When the agglomeration level exceeds 1.963, the expansion of agglomeration scale will increase local carbon emissions. For underdeveloped tourism areas, there is an inverted U-shaped relationship between agglomeration level and carbon emissions both in local and adjacent areas. When the agglomeration level reaches 1.883, the expansion of agglomeration scale will reduce local carbon emissions. The conclusions provide a basis for Chinese government to guide the low-carbon development of the tourism industry from another perspective. [ABSTRACT FROM AUTHOR]

The anthropogenic emission of carbon dioxide (CO2) into the atmosphere is recognized as the main contributor to global climate change. To date, scientists have developed various strategies, including CO2 utilization technologies, to reduce global carbon emissions. This paper presents the global scientific landscape of the CO2 utilization research from 1995 to 2019 based on a bibliometric analysis of 1875 publications extracted from Web of Science. The findings indicate a major increase in the number of publications and citations received from 2015 to 2019, denoting a fast-emerging research trend. The dynamics of global CO2 utilization research is partly driven by China's policies and research funding to promote low-carbon economic development. Applied Energy is recognized as a core journal in this research topic. The utilization of CO2 is a multidisciplinary topic that has progressed by multidimensional collaborations at the country and organizations levels, while the formation of co-authorship networks at the individual level is mostly influenced by the authors' affiliations. Keyword co-occurrence analysis reveals a rapid evolution in the CO2 utilization strategies from chemical fixation in carbonates and epoxides to pilot-scale testing of power-to-gas technologies in Europe and the USA. The development of efficient power-to-fuel technologies and biological utilization routes (using microalgae and bacteria) will probably be the next research priorities in CO2 utilization research. [ABSTRACT FROM AUTHOR]

Carbon footprint (CF) research has attained tremendous popularity for improving the climate environment purposes. In particular, current energy use has been identified as the main cause of climate change. CF plays an irreplaceable role in managing energy use, reducing gas emissions, and improving climate change. The objective of this study was to review studies that have developed CF and to perform a bibliometric analysis using two key terms: "climate change" and "energy use". From bibliometric analysis using CiteSpace and VOSviewer, it was possible to establish a knowledge map of cooperative network structure and research evolution. We are aiming to reveal the main logical chain of CF research leading to climate change, to make up for the lack of current literature, and provide research inspiration for researchers. The research findings mainly focus on four aspects. First, the relevant research began in 2008 and is in a state of continuous rise. Second, due to the law of research development and the prominence of practical problems, related research has experienced a stage from conceptual methods to specific problems. Third, China and the USA assume an important role in which international cooperation is the overall trend. Fourth, related research can be divided into CF algorithm research, ecological environment management research, and specific cross-industry fields. In addition, possible opportunities for change in related research are explored. It is also suggested that the integration of CF with other footprints, when energy use and environmental change are fully considered, may become an important future research trend by providing a more comprehensive environmental impact. [ABSTRACT FROM AUTHOR]

The climate change on the impact of grain production potential has significant regional differences. Researchers have studied the grain production potential of various crop combinations or focused on single crop types in a typical area; however, the regional differences of the climate change on the impact of grain production potential were neglected. This paper used the Global Agro-Ecological Zone (GAEZ 3.0) model to focus on the analysis what is the climate change on the impact of grain production potential in different geographic units (Northern Shaanxi Plateau, Guanzhong Basin, Qinba Mountain) in Shaanxi Province of China. The case showed that the precipitation (Pre) what made changes of grain production potential was the most important factor in different geographic units. The increase of Pre had a positive impact on the grain production potential in Northern Shaanxi Plateau and Guanzhong Basin. However, in Qinba Mountain, due to excessive Pre in the Qinba Mountains, the decrease of Pre had a certain positive impact on the grain production potential. The precipitation was less in the Northern Shaanxi Plateau; therefore, its major factors leading to changes of crop production were precipitation and rainfall days. The increase of the mean maximum temperature (Tmx) and the mean minimum temperature (Tmn) had a positive impact of the grain production potential in the Northern Shaanxi Plateau and Guanzhong Basin. The higher temperature had a negative impact on the grain production potential. In Qinba Mountain, the increase of the temperature has a certain negative impact on the grain production potential. It has more influence of Tmx in the Guanzhong Basin and Qinba Mountain rather than that in the Northern Shaanxi Plateau. Generally speaking, the major climatic factors leading grain production potential were Pre and Tmx in Guanzhong Basin and Qinba Mountain. [ABSTRACT FROM AUTHOR]

Vegetation is a natural tie that connects the atmosphere, hydrosphere, biosphere, and pedosphere. Quantitatively evaluating the variability of vegetation coverage and exploring its associated influence factors are essential for ecological security and sustainable economic development. In this paper, the spatiotemporal variation of vegetation coverage and its response to climatic factors and land use change were investigated in the Yangtze River Delta (YRD) from 2001 to 2015, based on normalized difference vegetation index (NDVI) data, vegetation type data, climate data, and land use/cover change (LUCC) data. The results indicated that the annual mean vegetation coverage revealed a nonsignificant decreasing trend over the whole YRD. Areas characterized by significant decreasing (P < 0.05) trends were mainly concentrated on the central and northern part of the YRD, and significant increasing (P < 0.05) trends were mainly located in the southern part of the study area. Except for grassland and cultivated crops, vegetation coverage of the other types of vegetation was all exhibiting increasing trends. Temperature has a more pronounced impact on vegetation growth than precipitation at both the annual and monthly scales. Furthermore, vegetation growth exhibited a time lag effect for 1~2 months in response to precipitation, while there was no such phenomenon with temperature. Land use change caused by urbanization is an important driving factor for the decrease of vegetation coverage in the YRD, and the effect of land use change on the vegetation dynamic should not be overlook. [ABSTRACT FROM AUTHOR]

As an important cause of global warming, CO2 concentrations and their changes have aroused worldwide concern. Establishing explicit understanding of the spatial and temporal distributions of CO2 concentrations at regional scale is a crucial technical problem for climate change research. High accuracy surface modeling (HASM) is employed in this paper using the output of the CO2 concentrations from weather research and forecasting-chemistry (WRF-CHEM) as the driving fields, and the greenhouse gases observing satellite (GOSAT) retrieval XCO2 data as the accuracy control conditions to obtain high accuracy XCO2 fields. WRF-CHEM is an atmospheric chemical transport model designed for regional studies of CO2 concentrations. Verified by ground- and space-based observations, WRF-CHEM has a limited ability to simulate the conditions of CO2 concentrations. After conducting HASM, we obtain a higher accuracy distribution of the CO2 in North China than those calculated using the classical Kriging and inverse distance weighted (IDW) interpolation methods, which were often used in past studies. The cross-validation also shows that the averaging mean absolute error (MAE) of the results from HASM is 1.12 ppmv, and the averaging root mean square error (RMSE) is 1.41 ppmv, both of which are lower than those of the Kriging and IDW methods. This study also analyses the space-time distributions and variations of the XCO2 from the HASM results. This analysis shows that in February and March, there was the high value zone in the southern region of study area relating to heating in the winter and the dense population. The XCO2 concentration decreased by the end of the heating period and during the growing period of April and May, and only some relatively high value zones continued to exist. [ABSTRACT FROM AUTHOR]

Global climate change and national policies play an important role in regional ecosystem services, both of which should be fully considered when exploring their effective use and management. Bayesian belief network (BBN) is often used in complicated system modelling. Using a BBN to construct a network framework of ecosystem services under climate and policy scenarios for exploring the total suitability distribution of ecosystem services is of great significance. In this study, we develop BBN for the total suitability of water yield and carbon sequestration based on hydro-biogeochemical process. And then we predict the probabilities of the total suitability in 2050 through the BBN under multi-scenario simulations accounting for climate change, birth control and carbon tax policies. Finally, total suitability priority regions are mapped, which are synergy development, water yield suitability, carbon sequestration suitability and non-suitability, respectively. Our results indicate forest, cropland, urban area, and grassland have the largest areas under the representative concentration pathway (RCP) 2.6, RCP 4.5, RCP 6.0 and RCP 8.5, respectively. The abolition of the one-child policy has led to a significant expansion of urban areas, and the implementation of the carbon tax policy has greatly increased forest areas. Additionally, temperature, Normalized Vegetation Index (NDVI), precipitation and land use are the key driving factors that influence suitability. The suitable priority regions of different alternatives help policy makers consider ecological protection priorities while addressing management options. [ABSTRACT FROM AUTHOR]

and Aim. An accurate estimation of biogenic emissions of VOC (volatile organic compounds) is necessary for better understanding a series of current environmental problems such as summertime smog and global climate change. However, very limited studies have been reported on such emissions in China. The aim of this paper is to present an estimate of biogenic VOC emissions during summertime in China, and discuss its uncertainties and potential areas for further investigations. This study was mainly based on field data and related research available so far in China and abroad, including distributions of land use and vegetations, biomass densities and emission potentials. VOC were grouped into isoprene, monoterpenes and other VOC (OVOC). Emission potentials of forests were determined for 22 genera or species, and then assigned to 33 forest ecosystems. The NCEP/NCAR reanalysis database was used as standard environmental conditions. A typical summertime of July 1999 was chosen for detailed calculations. The biogenic VOC emissions in China in July were estimated to be 2.3×1012gC, with 42% as isoprene, 19% as monoterpenes and 39% as OVOC. About 77.3% of the emissions are generated from forests and woodlands. The averaged emission intensity was 4.11 mgC m-2 hr-1 for forests and 1.12 mgC m-2 hr-1 for all types of vegetations in China during the summertime. The uncertainty in the results arose from both the data and the assumptions used in the extrapolations. Generally, uncertainty in the field measurements is relatively small. A large part of the uncertainty mainly comes from the taxonomic method to assign emission potentials to unmeasured species, while the ARGR method serves to estimate leaf biomass and the emission algorithms to describe light and temperature dependence. This study describes a picture of the biogenic VOC emissions during summertime in China. Due to the uneven spatial and temporal distributions, biogenic VOC emissions may play an important role in the tropospheric chemistry during summertime. . Further investigations are needed to reduce uncertainties involved in the related factors such as emission potentials, leaf biomass, species distribution as well as the mechanisms of the emission activities. Besides ground measurements, attention should also be placed on other techniques such as remote-sensing and dynamic modeling. These new approaches, combined with ground measurements as basic database for calibration and evaluation, can hopefully provide more comprehensive information in the research of this field. [ABSTRACT FROM AUTHOR]

Understanding the evolution of runoff and identifying the main driving force of hydrological cycle changes are essential for water resource management. In this study, the spatial and temporal patterns of climate variables and hydrological factors were explored by applying geostatistical analysis and trend analysis in the Beiluo River Basin (BRB), China, and conversions of land use/cover change (LUCC) were assessed using chord diagrams. Contributions of climate change and human activities to runoff change were quantified employing multiple methods. The results show that annual precipitation and actual evapotranspiration increased significantly during the impact period (2004–2014) (p < 0.05), at rates of 19.3 mm/a and 11 mm/a, respectively, and there was a minor upward trend in annual runoff, at a rate of 0.38 mm/a, while annual potential evapotranspiration decreased slightly at a rate of − 3.3 mm/a. Climate variables were the primary contributor to runoff decrease from 2004 to 2011, with an average contribution of − 79% according to the three methods. Human activities were estimated to account for − 81% of runoff change from 2012 to 2014, which was inextricably linked to the increasing LUCC. The results of this study can provide a theoretical basis for regional water resource management under the influence of climate change and human activities. [ABSTRACT FROM AUTHOR]

The nations participating in the Belt and Road Initiative (BRI) are particularly vulnerable to the challenges posed by climate change due to their extensive trading activities. The need to protect the environment and mitigate the adverse impacts of climate change in these countries is of utmost importance. Therefore, this study contributes to the scientific understanding of this issue by examining the relationship between trade openness and environmental sustainability in 89 BRI countries from 1990 to 2020. Additionally, control variables, including economic growth, energy consumption, urbanization, industrialization, and foreign direct investment, are considered to address omitted variable bias issues. The study utilizes the Augmented Mean Group (AMG) and Common Correlated Effects Mean Group (CCEMG) regression estimators, and the findings reveal that trade openness improves environmental sustainability. However, economic growth, energy consumption, urbanization, and industrialization degrade environmental sustainability. Interestingly, the results affirm foreign direct investment as a trivial determinant of environmental sustainability. Regarding causal relationships, reciprocal causalities are observed between trade openness and carbon emissions, energy consumption and carbon emissions, and urbanization and carbon emissions. Furthermore, one-way causalities exist from economic growth to carbon emissions and from carbon emissions to foreign direct investment. Nevertheless, no causal relationship is identified between industrialization and carbon emissions. Based on these significant findings, it is recommended that China, as a prominent player in the BRI, takes further steps to enhance and promote energy-efficient practices in BRI countries. One practical approach is the establishment of energy efficiency standards for the goods and services traded with these countries. [ABSTRACT FROM AUTHOR]

Zooplankton community could be affected by aquatic environment, land use patterns, and climate factors. However, few studies have evaluated the relative importance of these factors to the zooplankton community succession on a long-term scale. In this study, long-term data were used to analyze the succession characteristics and drivers of zooplankton communities in Honghu Lake, a large-scale shallow lake in central China. Results showed that zooplankton community structure changed greatly in 1959–2021. In general, the species number decreased, abundance and biomass increased, and the number and proportion of pollution tolerant species among dominant species increased during the past ~ 60 years. For the driving factors of zooplankton from 1992 to 2021, redundancy analysis showed that nutrients, wind speed, and aquaculture enclosure and pond were the main drivers of Protozoa and Rotifera, while phytoplankton abundance, wind speed, dissolved oxygen, and farmland were the main drivers of Cladocera and Copepoda. Generalized additive model showed that the aquatic environment had the highest direct explanation (mean is 60.2%) to zooplankton variation, followed by land use (mean is 35.6%) and climate factors (mean is 33.1%). Variance partitioning analysis showed that the effects of human activities (32.9–43.1%) associated with land use, water quality, and phytoplankton on the zooplankton succession are stronger than that of climate factors (19.9–29.2%). Additionally, lake eutrophication is an important factor for the decline in the richness index (p = 0.0017, r = − 0.8411) and Shannon index (p = 0.0301, r = − 0.8020) of zooplankton from 2019 to 2021. Our results highlighted that anthropogenic activity has stronger driving effects on zooplankton succession in the long term than climate changes in the lake. These findings can provide insights into zooplankton conservation in response to global climate change and complex anthropogenic actions. [ABSTRACT FROM AUTHOR]

Ecological security is the state achieved once an ecosystem maintains its stability under external stress. The Yellow River Basin (YRB) is the largest river basin in northwest and north China and an important area for grain and energy production. The assessment and attribution of ecological security in the YRB are important for protecting the natural environment and ensuring sustainable development. Here, the ecological security of the YRB was assessed by the ecological security index (ESI), a comprehensive index based on the oxygen cycle, and its drivers were attributed to climate change, human activities, vegetation, and soil factors. The spatial pattern of ecological security in the YRB showed high heterogeneity. Ecological insecurity occurred mainly in the middle reaches and regions where the major stream of the Yellow River passes through. The ESI decreased at a rate of − 0.82/year since 2000, which indicated the natural environment continued to be improved in the YRB. Climate change dominated the evolution of ecological security in the upper reaches. The level of ecological security has been improved in the middle reaches after a series of ecological restoration projects conducted. With higher intensity of industrial activity, human activities played a more critical role in ecological security in the lower reaches. Our results suggested that government and local people need to adopt different strategies and actions based on the dominant drivers in the upper, middle, and lower reaches to ensure protection of the natural environment and achieve sustainable development targets. [ABSTRACT FROM AUTHOR]

The corresponding relationships between temperature, precipitation, and polycyclic aromatic hydrocarbon (PAH) concentration in a typical ombrotrophic peatland in Dajiuhu, Shennongjia, were quantitatively characterized by field sampling tests validated with simulation experiments. The PAH concentrations of peat cores in Dajiuhu peatland ranged from 262 to 977 ng·g−1, with a mean value of 536 ± 284 ng·g−1. PAHs were mainly composed of 2–3 ring PAHs, accounting for 31.7% ± 2.00% and 31.7% ± 5.00%, respectively. The concentration of PAHs in peat cores showed a significant decrease with increasing temperature, while the low molecular weight PAHs (LMW-PAHs) were more sensitive to temperature changes compared to the high molecular weight PAHs (HMW-PAHs). Besides, with the increase of quantity and velocity of leaching liquid, PAHs in peat were first transferred in the form of attached large-size particles and then gradually entered the aqueous phase. According to the IPCC projections of global warming, Dajiuhu peatland will release 956 ± 26.3 kg·°C−1 PAHs into gas phase during 2030–2052, and a conservative projection based on local temperature trends showed that 459 ± 12.6 kg·°C−1 PAHs will be released into gas phase by 2047 in Dajiuhu peatland. The projected release fluxes of PAHs in Dajiuhu peatland with precipitation volume and precipitation velocity are 381 ± 201 kg·100 mm−1 and 1052 ± 167 kg·min·mL−1, respectively, which are primarily from peat into particulate and aqueous phase. [ABSTRACT FROM AUTHOR]

Climate data with high spatial and temporal resolution were of great significance for regional environmental management, such as for early response to possible predicted local climate changes and extreme weather. However, the current downscaling targets for CMIP6 climate simulations were mostly medium-resolution (MR) reanalysis data, which were still coarse for local analysis. A two-step downscaling method was proposed for 100 × resolution enhancements of general circulation model (GCM) daily temperature data in this study. First, the historical GCM outputs were 10 × downscaled to a set of dynamically predictable MR data using a deep convolutional neural network (CNN), which included both encode-decode structure and long-short skip connections. Then, using high-resolution (HR) topographic data and MR climate data as auxiliary data, the GCM data were super-resolved to a series of images with spatial resolution of 1 km. A one-step downscaling analysis combined only with HR topographic data was performed as comparison. Seven evaluation metrics were selected to evaluate the prediction accuracy, and the results showed that the overall performance of two-step downscaling method was better than one-step downscaling method. Higher Nash–Sutcliffe efficiency (NSE) and lower mean absolute relative error (MARE) indicated that the two-step method performed better prediction of peak and low values. It was further confirmed by accuracy evaluation on the 10% max and 10% min values of the testing dataset. The introduction of dynamically predictable MR data could provide effective detailed information during the downscaling process and improve the prediction accuracies. Finally, the projected data of four scenarios (SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5) during 2015–2050 were downscaled to the study area. The complex temporal and spatial variations indicated that there were great differences in temperature changes in a basin, and differentiated management measures should be proposed in advance. [ABSTRACT FROM AUTHOR]

Lake organic matter is one of the important forms of terrestrial carbon, and its sedimentary evolution is affected by many factors such as climate and sources. However, few studies have been conducted on the feedback mechanism of the sedimentary evolution of organic matter to climate change in cold and arid lakes. Historical variations and compositions of sources of the sediment organic matter (SOM) of Hulun Lake, a typical lake in the cold and arid region of China, were studied by multiple methods. The interactions and fee7dback mechanisms between the sedimentary evolution of SOM and climate change, and compositions of SOM source change, were also discussed. Overall, the characteristic indexes of the SOM, including total organic carbon (TOC), carbon stable isotope (δ13C), carbon to nitrogen ratio (C/N), and fluorescence intensity (FI) of the protein-like component in water extractable organic matter (WEOM), showed obvious and uniform characteristics of periodical changes. The indexes were relatively stable before 1920, and fluctuated from 1920 to 1979. Since the 1980s, values of TOC, δ13C, and FI of the protein-like component in WEOM has increased, while C/N decreased. The absolute dominant contribution of terrestrial source to the SOM had changed, and the relative average contribution rate of autochthonous source increased from 17.6% before 1920 to 36.9% after 2000. The increase of temperature, strong evaporation concentration effect, and change of compositions of SOM sources are the important driving factors of the sedimentary evolution of organic matter in Hulun Lake. [ABSTRACT FROM AUTHOR]

A variety of physical, chemical, and biological processes within the lakes relies on the surface water temperature while the spatial pattern of large lakes of different warming trends and their connections with climate change remain unclear. Using correlation analysis, regression tree analysis (RTA), and general linear models (GLMs), we have estimated the warming trends of 192 lakes since 2000 in the populated Yangtze River Basin, China, to identify dominant climate drivers and quantify their contributions. The results show that surface water temperature has increased substantially in the majority of the investigated lakes (179 from a total of 192 lakes) at a rate of 0.29 (− 0.12 to 0.62) °C/decade (median and 95% confidence interval). The shallower lakes (< 13.1 m in depth) usually have the faster median warming rates than the deeper lakes (i.e., 0.37 °C/decade versus 0.16 °C/decade). We find that in the shallow lakes, rising air temperatures and declining wind speeds can explain the majority of variation in surface water temperature (i.e., 31.4‒80.3% and 13.0‒21.0%, respectively). In contrast, in deeper lakes, change of air temperatures plays a dominant role in water warming (75.4‒91.2%). This study has emphasized the importance of declining wind speed in water warming in large and shallow lakes and illustrated a difference of dominant climatic drivers in water warming between the shallow and deep lakes. [ABSTRACT FROM AUTHOR]

This study analyzed the determinants that impact innovation on offshore wind energy (OSW) for a select group of countries, applying mixed-method approaches for a period between 2011 and 2021. The OSW sector witnessed cost reductions in recent years; therefore, this study analyzed how these factors impact technological innovation in the OSW sector. The fixed effects results proved trademark, carbon emissions, offshore wind capacity, and electricity from renewable energy, technical and scientific journals are significant and impact innovation regarding offshore wind energy. The maximum likelihood (MLE) coefficients are more robust than the restricted maximum likelihood (MREL) and better explained the significance of the variables in spurring OSW innovation. Ultimately, the interaction term "cross" came out significant in the analysis. It signified the importance of the interaction variables in scaling innovation. Similarly, the study forecasted OSW capacity additions to grow to more than 28GW by 2036, at a 48.8% growth rate, from the current over 55 G.W. capacity. Additionally, the infrastructure development of the OSW sector via a fitted line between total global offshore capacity and the development projects observed a negative relationship among these dual factors evaluated in the OSW, showing a decreasing trend of capacity additions among countries, as well as the fitted line relationship between total country OSW capacity and operational projects showed that China leads the globe with operational OSW projects. At the same time, Brazil is the new leader in the world regarding OSW capacity. The general analysis of the parameters of the OSW infrastructure plan showed the sector is declining regarding secured projects, development projects, operational projects, and capacities additions in some countries. However, global total capacity additions are on a steady path, declining a bit. This is attributable to the pandemic that slowed the global economy. This study will serve as a reference document for policy formulators regarding scaling up innovation for offshore wind energy. [ABSTRACT FROM AUTHOR]

The Pearl River Basin (PRB) is a significant area for economic development (ED) and ecological protection in China. Studying the relationship between carbon emission (CE) and ED is crucial for China and the world to cope with climate change and achieve CO2 reduction. For 48 cities in the PRB, we used the coupling coordination model and geographically weighted regression model to analyze the coupling coordination degree (CCD) between CE and ED and investigate the main influencing factors. The results suggested that (1) the CCD presents spatial heterogeneity, with the Pearl River Delta having the highest value and the middle reaches having the lowest value; (2) the coupling coordination type between CE and ED changes from incoordination to coordination in general; and (3) the resident income and population size have a positive influence on the CCD of the cities in the lower reaches, while the secondary industry scale has a beneficial impact on the upstream. Finally, we put forward corresponding policy suggestions to achieve sustainable development in terms of reducing economic inequities, enhancing public expenditure and innovation capability, and streamlining the industrial structure. [ABSTRACT FROM AUTHOR]

Intensifying climate change significantly impacts residential electricity consumption, especially in developing countries, such as China, that are experiencing rapid income growth. By combining meteorological and monthly household consumption survey data, this study explores the response function of residential electricity consumption to temperature in China from a micro perspective. Future residential electricity demands and related CO2 emissions are then forecast under different climate scenarios. Overall, the response function is U-shaped, and one additional day above 34 °C will increase monthly residential electricity consumption by 2.11%. Global warming will more likely increase the electricity burden on low-income groups. There will be notable seasonal changes in electricity demand in the future, and the largest increase will occur in August. The total demand for residential electricity caused by temperature change will show a fluctuating growth trend, from 0.8% and 1% in 2025 to 2% and 2.9% in 2060 under the RCP4.5 scenario and RCP8.5 scenario, respectively; meanwhile, this demand will be accompanied by a cumulative increase in carbon dioxide emissions. [ABSTRACT FROM AUTHOR]