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Optimizing crop distribution stands as a pivotal approach to climate change adaption, enhancing crop production sustainability, and has been recognized for its immense potential in ensuring food security while minimizing environmental impacts. Here, we developed a climate-adaptive framework to optimize the distribution of staple crops (i.e., wheat, maize, and rice) to meet the multi-dimensional needs of crop production in China. The framework considers the feasibility of the multiple cropping systems (harvesting more than once on a cropland a year) and adopts a multi-dimensional approach, incorporating goals related to crop production, water consumption, and greenhouse gas (GHG) emissions. By optimizing, the total irrigated area of three crops would decrease by 7.7 % accompanied by a substantial 69.8 % increase in rain-fed areas compared to the baseline in 2010. This optimized strategy resulted in a notable 10.0 % reduction in total GHG emissions and a 13.1 % decrease in irrigation water consumption while maintaining consistent crop production levels. In 2030, maintaining the existing crop distribution and relying solely on yield growth would lead to a significant maize production shortfall of 27.0 %, highlighting a looming challenge. To address this concern, strategic adjustments were made by reducing irrigated areas for wheat, rice, and maize by 2.3 %, 12.8 %, and 6.1 %, respectively, while simultaneously augmenting rain-fed areas for wheat and maize by 120.2 % and 55.9 %, respectively. These modifications ensure that production demands for all three crops are met, while yielding a 6.9 % reduction in GHG emissions and a 15.1 % reduction in irrigation water consumption. This optimization strategy offers a promising solution to alleviate severe water scarcity issues and secure a sustainable agricultural future, effectively adapting to evolving crop production demands in China., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

Rice production is a primary contributor to global greenhouse gas emissions, with unclear pathways towards carbon neutrality. Here, through a comprehensive assessment of direct greenhouse gas (GHG) emission using DNDC model and indirect GHG emission using emission factor methods, we estimated the annual crop yield, GHG emission amount and intensity, and economic benefits of different cropping patterns in the climate-sensitive regions of rice production in China. Through the expansion of single-rice and cropping pattern change from the wheat-rice to wheat-rice-rice in the climate-sensitive regions of single and triple-cropping cultivations, the total grain yield increased by 4.4 % and 4.5 % compared with the current national grain production, the GHG emission would increase by 2.4 % and 5.4 % of the current national GHG emissions from rice and wheat production, the net economic benefits could increase 0.9 % and decrease 2.0 % of the national output value of rice and wheat production. The study takes the entire-life cycle of crop growth as the principal line, and could provide a valuable reference for the regulation of the cropping pattern and the formulation of carbon reduction policies in the climate-sensitive region., 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 B.V.)

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.)

CO2 is one of the primary greenhouse gases impacting global climate change, making it crucial to understand the spatiotemporal variations of CO2. Currently, commonly used satellites serve as the primary means of CO2 observation, but they often suffer from striping issues and fail to achieve complete coverage. This paper proposes a method for constructing a comprehensive high-spatiotemporal-resolution XCO2 dataset based on multiple auxiliary data sources and satellite observations, utilizing multiple simple deep neural network (DNN) models. Global validation results against ground-based TCCON data demonstrate the excellent accuracy of the constructed XCO2 dataset (R is 0.94, RMSE is 0.98 ppm). Using this method, we analyze the spatiotemporal variations of CO2 in China and its surroundings (region: 0°–60° N, 70°–140° E) from 2019 to 2020. The gapless and fine-scale CO2 generation method enhances people's understanding of CO2 spatiotemporal variations, supporting carbon-related research. [ABSTRACT FROM AUTHOR]

The global value chain has introduced profound changes in international trade, economic development, and technology progress as well as greenhouse gas emissions worldwide. This paper investigated the impact of the global value chain and technological innovation on greenhouse gas emissions by introducing a partially linear functional-coefficient model based on panel data of 15 industrial sectors in China from 2000 to 2020. Moreover, the greenhouse gas emission trends of China's industrial sectors from 2024 to 2035 were predicted using the autoregressive integrated moving average model. The results showed that (1) Greenhouse gas emissions were affected negatively by global value chain position and independent innovation. Nevertheless, foreign innovation had the opposite effect. (2) The results of the partially linear functional-coefficient model implied that the inhibitory effect of independent innovation on GHG emissions decreased with an improvement in the global value chain position. (3) The positive effect of foreign innovation on greenhouse gas emissions increased and then, decreased as the global value chain position improved. (4) The prediction results indicated that greenhouse gas emissions will continue on an upward trend from 2024 to 2035, while industrial carbon dioxide emissions should peak at 10.21 Gt in 2028. This carbon-peaking goal would be achieved in China's industrial sector by actively improving the global value chain position. Addressing these issues will enable China to take full advantage of the development opportunities of participating in the global value chain. [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]

China has established three rounds of low-carbon pilot city initiatives. However, a gap exists between policy making and successful implementation, and the effects on greenhouse gas (GHG) emission reduction were found to be different among pilots. This paper considers the question of how low-carbon policies in cities can be implemented effectively and how policy combinations are adopted for GHG emissions reduction. It uses a mixed research approach, including Qualitative Comparative Analysis (QCA) and field investigation in Pilot Y (situated in the southwest China). The results show that, first, financial support and monitoring systems are necessary conditions, while political leadership is a sufficient condition. This means that the absence of financial support or monitoring systems could lead to increased GHG emissions, while the presence of political leadership always promotes the reduction of GHG emissions but may not be sufficient on its own to achieve them. Second, the target responsibility system (TRS) – a system used by higher government levels to assess the policy implementation of lower government levels – and human resource mechanisms cannot effectively lead to the promotion of GHG emissions reduction. Finally, in the absence of TRS and human resource mechanisms, environmental information disclosure (EID) was found to effectively promote low-carbon urban development by encouraging public participation. This paper reveals the complexity of the implementation of low-carbon policies in cities, identifies the link between city-level and national action, and enriches the theoretical explanation of governance and low-carbon policy implementation gaps in China. Key policy insights China's low-carbon pilot city project is critical for reducing GHG emissions, however, the effects of the low-carbon pilot city programmes varied greatly. Financial support and monitoring systems are necessary conditions, while political leadership is a sufficient condition to achieve cities' GHG emissions reduction goals. Local low-carbon development is limited and not put into practice at the city level in China, in part because of the absence of financial support and limited political attention. This study highlights the key conditions of organizational behaviour at the meso-level, and enriches the theoretical explanation of environmental policy implementation gaps in China. [ABSTRACT FROM AUTHOR]

Global climate change caused by greenhouse gas emissions has a direct impact on the sustainable development of human society and has gained widespread concern globally. Biological carbon sequestration measures, mainly forests, are conducive to improving the ecological carbon sink capacity and play an important role in mitigating global climate change. Therefore, assessing the efficiency of forest carbon storage (FCS) is key. In view of the lack of evaluation methods for forest carbon storage efficiency (FCSE) on a large spatial scale and long time series, a method based on Data Envelopment Analysis (DEA) was proposed in this paper. The FCS was calculated using the forest storage expansion method, and the FCSE was calculated using the DEA-Malmquist index to solve the dynamic balance between regional forestry economic input and carbon storage output efficiency. The FCSE in Chinese provinces was analyzed. The results showed that from 1999 to 2018, China's FCS increased annually, from 7558.34 million tons to 9476.73 million tons, and the spatial distribution pattern of total FCS was always different among regions. The average TFP value of FCSE was 1.089, which proves that the FCSE in China has been on the rise in the past 20 years, but the efficiency growth differed between provinces and was affected by different factors such as technical efficiency and technological progress. [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]

Assuming responsibility for emission reduction is an important part of participating in the global governance of climate change. Countries with a carbon neutrality target now cover about 75% of global greenhouse gas emissions. Carrying out fairness evaluations of their carbon neutrality targets provide a strong basis for the global carbon inventory. In this paper, the target years of carbon neutrality for major countries and regions under the global target of 2 °C and 1.5 °C are studied using the burden sharing model. On this basis, this paper simulates the possible pathways of carbon neutrality and evaluates the ambition of emission reduction targets in terms of annual emission reduction, per capita emission, and cumulative per capita emission. The results show that, from the perspective of equity, most of the developing countries cannot meet the global goal of 1.5 °C. The United States and the European Union nations could achieve carbon neutrality by 2050 by continuing their annual emission reductions under their existing NDC targets. However, to achieve carbon neutrality by 2060, China's annual emission reduction rate from 2030 to 2050 will be 6.7%, and the annual amount of emission reduction accounts for 51% of the global amount under the 1.5 °C target. Meanwhile, China would become the main force behind global cumulative emissions in the following decades. Thus, the emission reduction pathways China chooses will have an important impact on global climate mitigation progress. Based on the above research conclusions, this paper proposes that developed countries and developing countries should explore bilateral or multilateral cooperation in technology and finance in the field of climate change. Under the great pressure to achieve carbon neutrality, China needs to carry out research on the overall emission reduction pathways as soon as possible and clarify the paths at the scale of sectors, industries, provinces, and time stages. [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]

As one of the common mulberry tree species, Morus notabilis C. K. Schneid plays a significant role in various industries such as silkworm rearing, papermaking, and medicine due to its valuable mulberry leaves, fruits, and wood. This study utilizes the maximum entropy (MaxEnt) model to predict the potential distribution of M. notabilis in China under future environmental changes. By integrating the relative percentage contribution score of environmental factors with jackknife test analysis, important variables influencing the distribution of M. notabilis were identified along with their optimal values. The results indicate that Annual Precipitation (bio12), Precipitation of Driest Month (bio14), Min Temperature of Coldest Month (bio6), Temperature Annual Range (bio5–bio6) (bio7), Precipitation of Warmest Quarter (bio18), and Precipitation of Coldest Quarter (bio19) are the primary environmental variables affecting its potential distribution. Currently, M. notabilis exhibits high suitability over an area spanning 11,568 km2, while medium suitability covers 34,244 km2. Both current and future suitable areas for M. notabilis are predominantly concentrated in Sichuan, Yunnan, and Guizhou provinces, as well as Chongqing city in southwest China. Under the SSP5-8.5 scenario representing high greenhouse gas concentrations by 2050s and 2090s, there is an increase in high suitability area by 2952 km2 and 3440 km2, with growth rates reaching 25.52% and 29.74%, respectively. Notably, these two scenarios exhibit substantial expansion in suitable habitats for this species compared to others analyzed within this study period. [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]

China's economy has made significant strides in the past three decades. As a direct result of China's "one belt, one road" (OBOR) initiative, the country's rate of industrialization and urbanization is currently the fastest in the entire world. This rapid development is largely dependent on the enormous amounts of energy currently being consumed and forms the foundation of the world's high levels of carbon emissions. It is generally agreed that the production of greenhouse gases, particularly carbon dioxide, is the primary contributor to the current state of climate change. In this paper, a CO2 emission prediction model based on Bi-LSTM is constructed. In order to conduct empirical tests on the model, this study uses data from South Asian countries and China from 2001 to 2020. China's CO2 emissions from 2022 to 2030 were predicted along with those of other countries in order to study the combined effects of the scientific and technological progress, industrial structures, and energy structure factors affecting CO2 emissions. When compared with the LSTM and GRU methods, the Bi-LSTM model's results produced lower MAE, MSE, and MAPE values, indicating that it performs better. According to the findings, carbon emissions represent a significant problem that will become much worse in the future due to China and India's high emissions, particularly in the next 10 years, if the government does not implement policies that help reduce those emissions. [ABSTRACT FROM AUTHOR]

Ceracris kiangsu (Orthoptera: Arcypteridae), is greatly affected by climatic factors and exhibits strong adaptability, posing a serious threat to the ecological environment. Therefore, predicting its potential suitable habitat distribution provides a proactive theoretical basis for pest control. This study using the Biomod2 package of R simulated and predicted the current and future potential distribution, area changes, changes in the center points of suitable habitats, and niche shifts of C. kiangsu under two different greenhouse gas emission scenarios, SSP1-26 and SSP5-85. The results show that: (1) Currently, the high suitability areas for C. kiangsu are mainly distributed in Yunnan, Jiangxi, Hunan provinces in southern China and phongsaly province in northern Laos. In the future, the center of the suitable habitat distribution pattern of C. kiangsu will remain unchanged, primarily expanding outward from medium and high suitability areas. Additionally, significant suitable habitats for C. kiangsu were discovered in Southeast Asian countries without previous pest records. (2) Compared to the present, the overall suitable habitat area for C. kiangsu is expected to expand, particularly under the SSP5-85 climate change scenario. (3) In the SSP1-26 and SSP5-85 climate scenarios, the geometric center of the suitable habitat generally shows a trend of gradually shifting northeast. (4) Under different climate scenarios, the suitable habitat of C. kiangsu has highly overlapping, indicating that the suitable habitat of C. kiangsu in the invaded areas is broader than in its native regions. In conclusion, the research findings represent a breakthrough in identifying the potential distribution areas of C. kiangsu, which is of great practical significance for the monitoring and control of C. kiangsu pest infestation in China and Southeast Asian countries. [ABSTRACT FROM AUTHOR]

Due to diverse methodologies and data sources, China's agricultural greenhouse gas (AGHG) emissions estimates demonstrate significant variability. A quantitative review was conducted on the prevalence and application of various accounting methods to clarify and reduce discrepancies in AGHG emissions estimation techniques and data application. Using a systematic PRISMA framework, 4257 global studies were analyzed, yielding 681 AGHG emission records from 57 papers and four databases explicitly focusing on China. The analysis reveals similarities between Chinese and global research trends, concentrating on environmental science, soil science, and ecology. Furthermore, the spatial scale and methodologies for China's AGHG emissions estimation are relatively centralized, with 53.21% of the research targeting site-specific scales and 74.12% employing inventory methods and statistical analysis. Regarding the variability in China's AGHG emission estimates between 1996 and 2020, estimates ranged from 100 to 800 Mt. C-eq, with the highest estimate being approximately 25 times the minimum within the same period. Notably, compared to the average AGHG emissions calculated for 1996–2020 (284.7 to 548.7 Mt. C-eq), estimates from the FAO and Our World in Data were higher by an average of 114.7% and 81.9%, respectively. These findings underscore the need to enhance remote sensing and survey data application and standardize AGHG emissions estimation methodologies and boundaries to ensure comparability across various accounting methods and datasets. This study conducts a quantitative analysis to identify and bridge the gaps in AGHG emission evaluation techniques, laying the groundwork for establishing a more comprehensive and resilient framework for AGHG emissions assessments. • An analysis of 4257 studies is conducted to assess uncertainties and discrepancies in AGHG emissions in China. • 53.21% of AGHG research targets site-specific scales; 74.12% uses inventory methods and statistical analysis. • The gap between different estimates of China's annual AGHG emissions is as high as 25 times. • Emissions estimates by the FAO and OWD exceed those from 57 scholarly articles by 114.7% and 81.9%, respectively. [ABSTRACT FROM AUTHOR]

While previous studies have largely focused on anthropogenic warming characterized by surface air temperature, little is known about the behaviors of human‐perceived temperature (HPT), which describe the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity and/or wind speed. Here we adopted an optimal fingerprinting method to compare seasonal mean HPTs in China with those from simulations conducted with multiple climate models participating in the Coupled Model Intercomparison Project Phase 6. We found clear anthropogenic signals in the observational records of changes in both summer and winter HPTs over the period 1971–2020. Moreover, the anthropogenic greenhouse gas influence was robustly detected, with clear separation from natural and anthropogenic aerosol forcings. The anthropogenic greenhouse gas forcing plays the dominant role (>90%) of human‐perceived warming. Urbanization effects contribute slightly and moderately to the estimated trends in summer and winter HPTs, respectively, in addition to the effects of external forcing. Plain Language Summary: Human influences have been identified in the observed warming quantified by surface air temperature (SAT), but SAT alone is inadequate as a metric for human thermal comfort. Here we focus on human‐perceived temperature (HPT), which describes the "feels‐like" equivalent temperature by considering the joint effects of temperature, humidity, and/or wind speed. We isolate anthropogenic impacts on the observed increase in summer and winter HPTs in China during 1971–2020 by comparing observations with state‐of‐the‐art climate models. Results show that the influence of anthropogenic greenhouse gas is detected, with clear separation from other external forcings such as solar and volcanic activities and anthropogenic aerosols. The human‐induced greenhouse gas increases are also found to explain most (>90%) of the observed human‐perceived warming. Along with the effects of large‐scale anthropogenic forcing, urbanization effects also have a slight to moderate influence on the estimated trends in summer and winter HPTs. Our work is an early attempt to provide quantitative evidence for the physiological impacts of anthropogenic global warming and local urbanization on human beings. Key Points: The warming is quantified by human‐perceived temperature that considers the joint effects of temperature, humidity and/or wind speedHuman influence could be robustly detected in both summer and winter human‐perceived warmingThe observed increase in human‐perceived temperature is mostly attributed to anthropogenic greenhouse gas increases [ABSTRACT FROM AUTHOR]

To achieve the goals of carbon peaking and carbon neutrality, China is actively promoting carbon reduction in many areas. Agriculture is one of the main sources of greenhouse gas emissions, and promoting the development of low-carbon agriculture is a critical way to achieve carbon reduction targets. Taking Chongming District in Shanghai as an example, this study summarizes the experience of low-carbon agricultural development in Chongming and analyzes the problems and challenges faced during its development. Finally, based on the system dynamics method, the causal relationship of carbon emission in Chongming's agricultural development is constructed, and feasible loop optimization suggestions are put forward. [ABSTRACT FROM AUTHOR]

Many agricultural regions in China are likely to become appreciably wetter or drier as the global climate warming increases. However, the impact of these climate change patterns on the intensity of soil greenhouse gas (GHG) emissions (GHGI, GHG emissions per unit of crop yield) has not yet been rigorously assessed. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, we found that climate change is expected to cause a 20.0% crop yield loss, while stimulating soil GHG emissions by 12.2% between 2061 and 2090 in China's agricultural regions. A wetter‐warmer (WW) climate would adversely impact crop yield on an equal basis and lead to a 1.8‐fold‐ increase in GHG emissions relative to those in a drier‐warmer (DW) climate. Without water limitation/excess, extreme heat (an increase of more than 1.5°C in average temperature) during the growing season would amplify 15.7% more yield while simultaneously elevating GHG emissions by 42.5% compared to an increase of below 1.5°C. However, when coupled with extreme drought, it would aggravate crop yield loss by 61.8% without reducing the corresponding GHG emissions. Furthermore, the emission intensity in an extreme WW climate would increase by 22.6% compared to an extreme DW climate. Under this intense WW climate, the use of nitrogen fertilizer would lead to a 37.9% increase in soil GHG emissions without necessarily gaining a corresponding yield advantage compared to a DW climate. These findings suggest that the threat of a wetter‐warmer world to efforts to reduce GHG emissions intensity may be as great as or even greater than that of a drier‐warmer world. Plain Language Summary: Both climate observations and projections suggest that warmer temperatures will intensify convective precipitation, resulting in drier regions becoming drier and wetter regions becoming wetter. By integrating an improved agricultural ecosystem model and a meta‐analysis of multiple field studies, this study has investigated the effects of drier‐warmer (DW) and wetter‐warmer (WW) climates on crop yield and soil greenhouse gas emissions in China. Our findings indicate that the cost of greenhouse gas emissions in a wetter‐warmer world for crop production could be as high as or even higher than in a drier‐warmer world. Moreover, this study suggests that effective climate mitigation practices need to account for the combined effects of extreme climate changes and nitrogen addition on greenhouse gas emissions and crop production. Therefore, China would need to exert considerably more effort in a future with warmer and wetter conditions to achieve its greenhouse gas emission reduction targets. Key Points: Climate change would cause a 20% crop yield loss and an increase of 12.2% in soil greenhouse gas (GHG) emissions between 2061 and 2090A Wetter‐Warmer climate would cause a more significant crop yield loss and about double the GHG emissions of a Drier‐Warmer climateNitrogen fertilizer‐induced GHG emissions in a Wetter‐Warmer climate would increase 37.9% compared to those in a Drier‐Warmer climate [ABSTRACT FROM AUTHOR]

Agroclimatic regionalization is an effective way to utilize agricultural climate resources in a reasonable manner. Accurate and objective agroclimatic regionalization plays a great directive role in ecological layout and decision support for agriculture farming. The purpose of this article was to investigate the influence of climate change on agroclimatic regions in China. Following the same regionalization principle concept as the theory and techniques of agroclimatic regionalization, three agroclimatic regions, the Tibetan High Cold Region (THCR), Northwest Arid Region (NAR), and Eastern Monsoon Region (EMR) were identified in China. The changes in the three agroclimatic regions were analyzed from 1961 to 2020 in the past and from 2006 to 2060 in the future. Future changes in agroclimatic regions were estimated from 2006 to 2030 and from 2031 to 2060 under scenarios RCP2.6, RCP4.5, and RCP8.5 for greenhouse gas emissions. The borders of agoclimatic regions had changed as a result of global climate warming and precipitation variability. There was a surplus in the geographical displacement and range of agroclimatic region borders in 1991 to 2020, especially when compared to those in 1961–1990. The TCHR exhibited significant spatial variation, with its northeast corner shifting nearly 170 km to the southwest. In the future, the area ratio of the THCR will be 26% by 2031–2060 under the RCP8.5 scenario, with the highest decline (1% compared to that in the reference period of 1981–2005), while under the RCP4.5 and RCP2.6 scenarios it will stabilize the area ratio at 27%. The eastern boundary of the NAR will migrate south or east, while the center boundary will rise northward. Under all three climatic scenarios, the area ratio will be 25% (down 1%). The range of the EMR will expand. The area ratio of the EMR will increase by 1% under the RCP2.6 and RCP4.5 scenarios and by 2% under the RCP8.5 scenario. Overall, our study may facilitate an in-depth understanding of agroclimatic regions changes and thus provide a scientific reference for the distribution of agricultural production and sustainable development under climate change in China. [ABSTRACT FROM AUTHOR]

The industrial sector has significantly contributed to global climate change and urban air pollution while driving economic development. This paper takes the synergistic CO2 and SO2 emission reduction in China's industrial sector as an entry point quantifies the synergistic CO2 and SO2 emission reduction effect using the Kaya extension model and analyzes the synergistic emission reduction expansion mechanism in depth. It is found that the synergistic effect of CO2 and SO2 in the industrial sector is prevalent in most provinces in China. Every 10,000 tons of CO2 emission reduction leads to 1600 tons of SO2 emission reduction. Regional heterogeneity also clarifies that there are differences in synergistic emission reduction among different provinces, with most regions having a significant positive effect of synergistic emission reduction, while a small number of regions have an inverse effect, and direct SO2 reduction measures should be considered separately. The interaction effect analysis found that energy efficiency improvement in the industrial sector is the critical path to synergistic effect enhancement. At the same time, R&D expenditure and fixed asset investment have a weak effect on synergistic emission reduction. The interaction of the three factors can synergize the emission reduction effect by more than twice. The study's findings can be used as a reference for China's industrial sector to achieve the goal of reducing pollution and carbon emissions and for the practice of regional synergistic emission reduction, as well as for the formulation of international synergistic emission reduction policies. [ABSTRACT FROM AUTHOR]

Among the technologies for the recovery of energy from waste, in particular residual municipal solid waste (rMSW), combustion is the most widely used thermo-chemical treatment process associated with thermal and electric power production by a steam cycle, named, shortly, Waste to Energy (WtE). Today, more than 500 WtE plants in the EU, about 400 in China and 76 in the USA are in operation, based on efficient technologies and advanced air pollution control systems. Energy recovery can be accomplished also by means of gasification; however, the presence of impurities together with the atmospheric pressure, at which syngas is normally produced, impose the feeding of syngas to a conventional steam cycle, leading to generally lower performances than WtE. The energy recovered by WtE offsets traditional energy sources such as fossil fuels and related emissions, providing savings in term of climate change. However, the savings obtainable by replacing electricity and/or heat will diminish as the energy systems will hopefully become increasingly renewable. Over this medium–long-term horizon, one possibility is to capture the CO2 from WtE flue gases and to store/use it. From the life cycle assessment perspective, it has been calculated that the introduction of CO2 capture and storage in WtE, despite energy penalties, is able to reduce the impact on climate change. The alternative approach, proposed to contain the emissions of greenhouse gases in the thermal treatment of waste, is using the carbon contained in it to produce commonly used chemical compounds (waste to chemicals). The benefits, in terms of reductions of greenhouse gases, are expected from the possibility of obtaining chemicals that can replace their analogue normally produced from fossil sources. To date, only one WtC demonstration plant is operating by being fed by rMSW-derived waste, and some similar initiatives are planned, but still adequate assurances in terms of robust knowledge of the involved complex processes, above all, if applied to highly inhomogeneous feed streams such as those obtained from rMSW, are not available. Once the several initiatives come to completion, it will enable waste management professionals to assess performance and to begin to consider such a facility in their planning. [ABSTRACT FROM AUTHOR]

Research on sustainable business models and climate change is the focus of this article, which examines the industry, business and sustainability which has attracted a lot of attention. Because of this, we are investigating the effects of implementing business sustainability capabilities using a multiple-case methodology. An original report on China's small- and medium-sized businesses provided the data for this study that examined the theoretical and empirical relationships between financial literacy, innovation and environmental conservation. We devised a conceptual model to demonstrate the impact of corporate sustainability skills and competencies on climate change. Endogeneity concerns connected to financial literacy, innovation and ecological sustainability necessitate the employment of an instrumental variable for climate change. After adjusting for individual and firm characteristics, we found that financial literacy may substantially impact a company's sustainable business model. The findings suggest that financial literacy may boost company innovations by relieving financial restrictions and enhancing corporate governance, both of which contribute to the development of sustainable businesses that assist in increasing climate change mitigation efforts. On the other hand, innovation has a more significant influence on low-leverage companies. There was also a lot of talk about climate change, greenhouse gas emissions and climate change in the business context. This research, therefore, sheds light on how business sustainability capabilities and competencies might improve a company's environmental performance. [ABSTRACT FROM AUTHOR]

Anthropogenic greenhouse gases (GHG) and anthropogenic aerosols (AA) have changed radiation balance and regulated the regional climate at seasonal or longer time scales. Based on station observations, reanalyses, and satellite observations, this study examines how anthropogenic forcings affect surface temperature and strongly regulate the regional climate at a diurnal time scale over the North China plains (NCP) in July and August. As AA cooling is dominant in the daytime over low-lying plains, it leads to a cooler day-warmer night temperature trend that decreases the diurnal temperature range over NCP but increases the thermal contrast between NCP and its west highlands. In response to the daytime cooling, the weakened vertical thermal contrast decreases the boundary layer turbulent mixing in the daytime and reduces friction to low-level winds, which leads to anomalous southerlies at 2000 LST over NCP. In contrast, nighttime warming results in anomalous northerlies at 0200 LST. On the other hand, in response to the enlarged horizontal thermal contrast, the stronger mountain-plain circulation helps to intensify low-level ascent over the plains at 0200 LST. These human-induced changes in the diurnal variation of regional circulations are conducive to the increased moisture convergence at 2000 and 0200 LST over NCP. The nighttime proportion of precipitation accordingly exhibits an increasing trend over NCP, though the seasonal precipitation decreases because of the weakened monsoon background. These findings highlight that the diurnal cycle of regional circulations can express a strong dynamic response to the radiation effect of anthropogenic forcings and thus affect the long-term change in regional climate. [ABSTRACT FROM AUTHOR]

Agriculture is a significant contributor to global greenhouse gas emissions, and reducing carbon emissions in this sector is essential for mitigating global warming. To achieve China's targets of carbon peak by 2030 and carbon neutrality by 2060, promoting low-carbon agricultural technology (LCAT) is fundamental. This study examines the impact of farmers' risk perception on LCAT adoption behavior and its productivity effects with the Ordered Probit regression method, using micro survey data from 531 farmers in Shaanxi Province, China. The results show that farmers with stronger risk perceptions were more likely to adopt LCAT, based on their loss aversion characteristics. Additionally, farmers' perceptions of yield, market, and climate risks positively influence the adoption of LCAT, with market risk perception having the strongest effect. Adopting LCAT has significant production and spillover effects, improving the output rate of farmers' operating farmland and neighboring plots by 2.4% and 1.2%, respectively, for each additional measure adopted. This study contributes to the perception and loss aversion literature by examining farmers' adoption of low-carbon agricultural practices. This study sheds light on the importance of risk perception in the adoption of sustainable agricultural practices and can inform policies aimed at promoting the adoption of LCAT for achieving sustainable agriculture and mitigating climate change, highlighting the crucial role of sustainable environmental management in the agricultural sector. [ABSTRACT FROM AUTHOR]

Human influence on regional warming since 1901 has received little attention because of limited data during the early period. This study investigates the relative contribution of different external forcings to observed annual, summer, and winter warming in China over the period 1901–2018. First, four observational datasets were compared to validate data representativeness, particularly during the early twentieth century. Observed temperature changes were then compared with outputs from phases 5 and 6 of the Coupled Model Intercomparison Project (CMIP5 and CMIP6) based on an optimal fingerprinting method. Generally, both generations of climate models were able to reliably reproduce long-term warming in China over the period 1901–2018; however, they slightly underestimate the amplitude of annual and winter temperature increases. The observed annual warming of 1.54°C from 1901 to 2018 was more rapid than the global mean and was mostly attributable to the anthropogenic forcing signal. The three-signal detection analyses, including greenhouse gas (GHG), anthropogenic aerosol (AA), and natural external (NAT) forcings, indicated the detectable and distinct influence of GHG and AA signals on annual, summer, and winter temperatures during 1901–2018. For annual mean temperature, the GHG and AA contributed to 2.06°C (from 1.58° to 2.54°C) and −0.45°C (from −0.17° to −0.73°C) of observed change, respectively. The GHG signal was detectable from individual CMIP6 models and thus was indicative of the robustness of this influence. While during 1951–2018, GHG and AA were simultaneously detected in the summer temperatures based on the CMIP6 models; here, the AA cooling effects offset approximately 25% of GHG-induced warming. [ABSTRACT FROM AUTHOR]

Aim: Comprehensive biodiversity protection necessitates the consideration of multiple indexes of diversity, and how the distribution patterns of priority areas may shift under climate change. Galliformes is a globally endangered avian order vulnerable to climate change that provide an important indicator for wildlife conservation effectiveness. Here, we identified priority areas for conserving Galliformes taxonomic, phylogenetic, and functional diversity in China and their spatial dynamics subject to climate change, and examined how well existing protected areas align with current and future priority areas. Location: China. Methods: We applied species distribution modelling and Zonation algorithms to identify conservation priority area dynamics for 47 galliform species across three biodiversity indexes subject to three future climate change scenarios to 2050s and 2070s. We overlaid these identified priority areas onto existing national nature reserves and national parks to assess and project their effectiveness. Results: Current priority areas proved spatially incongruent between indexes, with an optimal area overlap comprising just 10.3% of China's land area, lying largely outside of existing protected areas. Furthermore, over 80% of modelled optimal priority areas currently lacked formal conservation status. Future priority areas will shift substantially under climate change, to an extent dependent on greenhouse gas emission scenarios. Nevertheless, we identified five large regions where optimal Galliformes diversity indexes should remain stable under all scenarios, thus providing potential climatic refugia, if protected from human encroachment. Main Conclusions: The current deficits we identified for Galliformes protection in China resonate with a broader need for hierarchical conservation strategic planning across regions and ecosystems to ensure long‐term biodiversity protection, accommodating for climate change. [ABSTRACT FROM AUTHOR]

Future changes in spatiotemporal features of the mean wind speed over China have been extensively reported, but future changes in the maximum wind speed, such as the daily maximum wind speed (DMWS), were rarely estimated. In this study, the performance of Coupled Model Intercomparison Project phase 6 (CMIP6) models in simulating the DMWS changes across China is evaluated, based on which the projection on DMWS is carried out under different shared socioeconomic pathways (SSPs). The observed DMWS shows a significant reduction during all four seasons, with the strongest decrease in spring and the weakest decrease in autumn. The DMWS increases from January to April and decreases from April to August. The spatiotemporal characteristics of the DMWS are captured by the multimodel ensemble of CMIP6; however, the reduction of DMWS in CMIP6 is weaker than those in observations. The performance of CMIP6 in simulating the future DMWS changes over China shows regional and seasonal discrepancies. The projected DMWS exhibits a reduction for all SSPs from 2021 to 2100, and the decreasing trend is increased accompanied by the strength in the forcing scenario. The lower-emission scenario likely avoids the long-term weakening of the DMWS. The effects of strength in forcing scenarios on the trends of DMWS are more significant than the DMWS climate state. The seasonal cycle of the projected DMWS under the different SSPs is consistent with the historical DMWS; however, the strength in the forcing scenario could induce enhanced variability in the month-to-month DMWS difference. [ABSTRACT FROM AUTHOR]

We first predicted the potential climate suitability of food crops with geographical indications (GIs) using the maximum entropy (MaxEnt) model and eight bioclimatic predicators under three shared socioeconomic pathway (SSP) scenarios during 2021-2040. The results showed that highly suitable rice, wheat, and corn areas increased in the three scenarios. Specifically, the growth rate under the SSP2-4.5 scenario was higher than that under the other scenarios. The potentially suitable area of wheat GIs was the largest with the most dramatic change. In contrast, the highly suitable area of millet and soybean GIs decreased under the SSP2-4.5 scenario but increased under the SSP1-2.6 and SSP3-7.0 scenarios. The increased greenhouse gas concentrations and radiative forcing under various scenarios had different effects on the dominant factors, changing the highly suitable areas of various food crop GIs. This study provides scientific evidence for the future cultivation of food crop GIs. [ABSTRACT FROM AUTHOR]

The diurnal temperature range (DTR) as measured by the difference between daily maximum (Tmax) and minimum (Tmin) temperatures is of great importance to human health, ecology, and agriculture. The link of its long-term change to anthropogenic forcing is still unclear. This study shows evidence of human influence on long-term changes in DTR over the globe, five continents, and China during the past century (1901–2014). Using multiple observational datasets, we find a general decrease in the DTR over most of the global land since 1901, especially after the mid-1950s. Changes in DTR are due to different warming rates of Tmax and Tmin in response to external forcings. The climate models that participated in phase 6 of the Coupled Model Intercomparison Project Phase 6 (CMIP6) generally reproduce most of the changes in DTR, along with those in Tmax and Tmin. The models have underestimated the observed changes in DTR, however. A formal detection and attribution analysis shows that the anthropogenic forcing signal, including both greenhouse gas and aerosol emissions but dominated by the greenhouse gas emissions, is the main driver for these changes. The anthropogenic aerosol signal can be detected in Tmax and Tmin but not in DTR during the period of 1901–2014 over the globe and most continents. These indicate the observed decrease in DTR is not a simple response to anthropogenic aerosol emission. The natural signal is negligible in almost all the cases. Globally, anthropogenic influence is estimated to explain more than 90% of the observed changes in the three variables. In China, human influence is also clearly detected, although model simulated results on the regional scale have larger deviation. Significance Statement: The diurnal temperature range (DTR) is of great importance in many areas. We compare multiple observational datasets with the simulations by climate models that participated in the latest phase (phase 6) of the Coupled Model Intercomparison Project (CMIP6), finding evidence of human influence on long-term changes in DTR over the past century (1901–2014) and robust evidence for the period since the early 1950s. The decrease in DTR as seen in the observational dataset is caused by different warming rates of daily maximum and daily minimum temperature in response to anthropogenic forcing, including both greenhouse gases and aerosols. [ABSTRACT FROM AUTHOR]

The cross-shelf exchange between the Yellow and East China Seas (YECS) with the Kuroshio is important for vorticity, freshwater, and effluence balances of the regional and global oceans, the responses of which to future climate changes are estimated based on Max-Planck coupled model simulations under one historical (20C, 1970–2005) and two representative concentration pathway scenarios of global warming (RCP4.5 and RCP8.5) from 2006 to 2099. The results of the 20C simulation have shown prominent cross-shelf transports in the YECS, with annual mean outflow (OVT) and inflow (IVT) volume transports across the 100 m isobath of 3.97 Sv and 2.86 Sv (1 Sv = 106 m3 s−1), respectively. These annual mean OVTs (and their equivalent IVTs) are projected to increase by 0.42 Sv and 0.48 Sv from 2006 to 2099, at rates of 11.0% and 12.8% from the 2006–2016 means, under the RCP4.5 and RCP8.5 scenarios, respectively. The maximum rates of increase are found in winter to be at 13.2% and 19.9%, respectively. Analyses suggest that the winter monsoon has a long-term southwesterly change, forcing coastal currents to flow into the Yellow Sea along the China coasts to compensate for the increase of the southeastward surface Ekman transport. Due to increases of the Tsushima Strait and upper-layer Kuroshio transports, both the OVT and IVT across the 200 m isobath have onshore changes, resulting in net onshore transport increments of 0.30 Sv and 0.57 Sv under the RCP4.5 and RCP8.5 scenarios, respectively. The enhanced cross-shelf exchanges under future warming scenarios in the YECS suggest their potential importance in carbon transportation and storage. [ABSTRACT FROM AUTHOR]

Possible thermodynamic effects of global warming on the landfalling typhoons that affect South China and their associated storm surges over Pearl River Delta region are investigated, using the Weather Research and Forecasting (WRF) Model and the Sea, Lake, and Overland Surges from Hurricanes (SLOSH) model based on the pseudo–global warming (PGW) technique. Twenty intense historical TCs that brought extreme storm surges to Hong Kong since the 1960s are selected and replicated by the 3-km WRF Model, with the outputs to drive the SLOSH model in storm surge simulation. The tracks, intensities, storm structure, and induced storm surges are well simulated. The PGW technique is then used to build a warmer background climate for the 20 selected TCs in the period of 2075–99 under the RCP8.5 scenario. To obtain a better adjusted warming environment, a pre-PGW adjustment method is developed. Comparing the same TCs in PGW experiments and historical runs, the TC lifetime peak (landfall) intensity can be intensified by about 9% 6 8% (12% 6 13%), with a ∼3% increase of TC peak intensity per degree of SST warming being inferred. The TCs are projected to be more compact, with the radius of maximum wind (RMW) reduced by ∼7% 6 10%. TC precipitation is also expected to increase, with the extreme precipitation within the eyewall strengthened by 22% 6 12%. All the above characters have passed the Student’s t test at 0.05 significance level. Finally, the projected induced storm surges near the Hong Kong waters are not significantly tested, although a weak storm surge height increase tendency is revealed. [ABSTRACT FROM AUTHOR]

We study the effect of human capital on CO2 emissions using the Chinese provincial panel over the period 1997-2016. Allowing for cross-sectional dependence and structural breaks, we find a negative association between human capital and CO2 emissions in the long run and attribute it to the influences from younger workers and workers with advanced human capital. In particular, our results suggest that a one-year increase in average schooling reduces CO2 emissions by 12 per cent. Using disaggregated emission dataset by energy sources and end emitters, we demonstrate this negative association is likely to manifest through technology effect and the improvement in energy efficiency. These manifestations are limited to production sector. Our finding suggests a promising avenue for abating greenhouse gases without impeding economic growth. [ABSTRACT FROM AUTHOR]

China and the United States are the world's top two greenhouse gas emitters. However, little is known as to the amount of attention the media in both countries have paid to the issue of climate change, and the ways in which their media framed the issue. To address both queries, this study analyzed the climate change coverage (N = 9,001) of the Chinese and US media, using a computational approach. Results revealed overall increases of attention to climate change in both countries' media from 2016 through 2019, whereas the Chinese witnessed a more evident increase after 2018. Moreover, the media in both countries tended to foster a sense of responsibilities rather than confining the issue to an episodic level. Furthermore, the US media highlighted political conflict and the catastrophic consequences of climate change, whereas the Chinese media highlighted public accountability and governance, as well as economic development and competitiveness. [ABSTRACT FROM AUTHOR]

In this study, precipitation, and temperature data from HadGEM2-ES under Representative Concentration Pathways (RCPs) 4.5 and 8.5 were used to evaluate drought in China in the 21st century. The K-means clustering algorithm was used to analyze the regional characteristics of the dry hazard index (DHI) in China, and the impact of climate change on the variation trend and periodicity of regional drought in China was explored. The results show that the temperature and potential evapotranspiration (PET) of all clusters have an increasing trend under the two RCPs, and the precipitation of most clusters shows a significantly increasing trend. The drought index calculated by the standardized precipitation-evapotranspiration index (SPEI) is higher than those calculated by the standardized precipitation index (SPI) and standardized effective precipitation evapotranspiration index (SP*ETI). The variation trends of drought intensity and frequency in China are not significant in the 21st century; however, the local variation trends are significant. The droughts in most parts of the Xinjiang Province, northern Tibet and western Qinghai Province show significantly increasing trends. According to the DHI analyses and the variations in the drought area ratio, with increases in greenhouse gas concentrations, the droughts in central and western China will become more severe, and drought will spread to the eastern areas of China. In the case that both precipitation and temperature may increase in the future, the increase in evapotranspiration caused by temperature rise will greatly affect drought dynamics. The main drought periodicity in China in the 21st century is 1~3.6 years. Drought is affected by climate change but not significantly. [ABSTRACT FROM AUTHOR]