Confronting the global environmental challenge of greenhouse gas emissions, China has proposed the strategy of "Carbon Peak" and "Carbon Neutrality" (i.e. the "Double Carbon" target). This research assessed the spatial–temporal differentiation of green innovation efficiency (GIE) and its driving factors of industrial enterprises above designated size (IEDSs) in the Yangtze River Economic Belt (YREB) under the target of "Double Carbon" for reducing carbon emissions. The research approaches a comprehensive assessment of GIEs and driving factors with an integrated evaluation index system built by the entropy weights. Furthermore, the entropy weight model, super-efficiency method, and geographic detector were employed to compute and estimate the differential efficiency and its changes for urban IEDS from both the dynamic and static perspectives. The driving factors contributing to spatial–temporal differentiation were analyzed and explored using the optimal parameter geographic detector. It was found that GIEs of IEDSs initially declined before increasing, with downstream areas outperforming the overall river basin. Decomposition of the GML index indicated that technological progress was the primary driver behind improvements in GIE. Spatial disparities in efficiencies gradually narrowed, evolving from lower to higher levels. The YREB was identified as a hot agglomeration area, while Guizhou and Yunnan were classified as cold agglomeration provinces. In the geographic detector analysis, technological progress was the most influential single factor, illustrating 0.604 of the driving forces. In contrast, the interaction between foreign direct investment (FDI) and technological progress exhibited the strongest dual-factor interaction, with an interpretation value of 0.831. This research demonstrated that the development trend of the estimated values of IEDSs’ GIEs in the YREB initially followed a downward trajectory and then differentiated. Overall, most provinces and cities showed improvements in their efficiency metrics before experiencing a subsequent upward resurgence. There was a severe spatial–temporal differentiation of IEDSs’ GIEs with driving factors. Some recommendations was further proposed for the YREB to improve the IEDSs’ GIEs and drive the sustainable development of IEDSs.

The breakthrough and widespread application of intelligent technologies highlight their potential to enhance and optimize the sustainable development capabilities of cities. Concurrently, improvements in urban sustainability levels reciprocally influence the advancement of intelligent technologies, driving continuous refinement and enhancement. This virtuous cycle presents a novel paradigm for urban development, offering solutions to address complex and evolving economic and environmental challenges. However, the intricate and multifaceted impacts of artificial intelligence (AI) on various foundational dimensions of cities remain underexplored. To address this gap, this paper constructs an urban sustainability index system and empirically analyzes the effects of AI on urban sustainability and its various dimensions, using data from 285 cities in China spanning the years 2011-2022. The study reveals the following key findings: (1) The levels of AI and urban sustainability in China exhibit spatial disparities, with eastern cities showing higher levels of AI, while cities with higher sustainability levels are concentrated in the southeastern and northeastern regions. (2) AI significantly enhances urban sustainability but disrupts coordination within the urban sustainability system (p < 0.05). (3) Robust digital infrastructure and a supportive market environment amplify the positive impact of AI on urban sustainability. (4) The positive impact of AI on urban sustainable development is most pronounced in large and medium-sized cities, non-resource-based cities, and those outside traditional industrial bases, likely due to the abundance of local resource endowments, which support AI development. (5) The influence of AI on urban sustainable development follows a nonlinear trend, initially increasing before decreasing. Based on these findings, we propose recommendations for optimizing regional AI deployment and developing context-specific AI technologies to enhance the sustainability levels of cities in China.

Evaluating the relationship between dynamic carbon emission intensity (CEI) and high-quality economic development (HQED) provides crucial insights for advancing national strategies focused on ecological preservation and sustainable high-quality development. This study employed an integrated analytical framework that combines the entropy-weight TOPSIS model, the coupling coordination degree (CCD) model, the spatial autocorrelation, and a two-way fixed effects model to examine the spatiotemporal patterns and influencing factors of carbon emissions in the Yangtze River Basin from 2010 to 2022. The results indicated that: (1) Temporal analysis revealed a consistent annual decline in CEI levels, coupled with steady improvements in HQED. The coordination between these two systems was reflected in the estimated CCD, and it showed an upward trend, with the lower reaches experiencing the most rapid progress in coordination. (2) Spatial analysis revealed a polycentric development pattern, with Shanghai serving as the central core, and other metropolises such as Nanjing and Hangzhou acting as secondary cores. The high–high agglomeration area has been progressively expanding each year. (3) Analysis of influencing factors revealed that their impacts diminished in the following order: human capital, economic development, urbanization, green innovation, government support, industrial structure, and openness. Each of these influencing factors demonstrated distinct spatiotemporal heterogeneity, varying in their impact across different regions and time periods. The study finally provided recommendations, emphasizing the need for coordinated development strategies in the YREB, taking regional dynamics into account, and promoting green economic transformations while ensuring ecological and environmental sustainability.

The development and interaction of resources, environment, economy, and society collectively determine the comprehensive development level of cities, while the advancement of smart cities exerts a profound influence on this complex system. Therefore, the primary aim of this study is to illustrate the impact of smart city construction on this system and its interrelationship by utilizing a PVAR model based on panel data from Chinese cities spanning from 2011 to 2022. The study finds that the level of smart city development in China has steadily improved, but its spatial distribution remains uneven, exhibiting a trend of expansion from the eastern coastal regions to the central and western areas. Smart city construction can enhance environmental, economic, and social levels in the short term, though it initially exerts negative effects on resources. Environmental development positively impacts the economy, society, and resources, yet it initially hinders the progress of smart city construction. By clarifying the relationships between smart city development and the aforementioned interactive systems, this study contributes to promoting a shift toward greener, low-carbon smart city development, which is crucial for achieving sustainable development.

The spatial–temporal pattern, influencing factors and driving variables of carbon emissions are essential considerations for achieving China’s carbon peak and neutrality targets, which support high-quality development. This study was designed to explore and evaluate the spatial–temporal evolutionary characteristics, trends and main influencing factors of carbon emissions in the Yangtze River Economic Belt (YREB), focusing on the decoupling of carbon emissions and socioeconomic development in the YREB. In total, 11 provinces and key cities were focused on as the research objects of the YREB district Tapio decoupling model, which examined the decoupling relationship between carbon emissions and socioeconomic development. Combined with a geographic detector, the Tapio, Logarithmic Mean Divisia Index (LMDI) and gray prediction models were employed in a comprehensive evaluating pipeline, which was constructed to decouple the main influencing factors and corresponding impacts of carbon emissions. Particularly, the gray prediction model was employed to predict the carbon emission differences in the YREB sub-regions in 2030. The results indicated the following: (1) The total carbon emissions showed a periodic fluctuation and upward trend with obvious spatial differences, and energy consumption was mainly dominated by coal. (2) The center of carbon emissions was located in Hubei Province in the middle reaches of the Yangtze River, with a standard deviation ellipse showing a “Southwest–Northeast” trend, and most provinces were concentrated in the L-H (low-high) cluster. (3) The entire YREB had achieved carbon emissions decoupling, but it was mainly in a weak decoupling state. (4) Carbon emissions were significantly affected by the indicator E for economic growth, with the indicators EI for energy consumption and I for the added ratio of GDP also bringing greater impacts on carbon reduction contributions. The carbon emission prediction results indicated that the upper and middle reaches of the YREB were more likely to achieve carbon neutrality.

The current problem of regional development imbalance remains severe, hindering further economic growth in China. The emergence of the digital economy provides new possibilities for achieving regional sustainable development. Leveraging panel data encompassing 31 Chinese provinces from 2011 to 2021, this research undertakes the construction of a comprehensive index framework elucidating the nexus between the digital economy and regional sustainability. Employing a suite of analytical tools including the coupled coordination model, panel fixed effects model, intermediary effect model, and threshold model, the study analyzes the impact of the digital economy on the level of regional sustainable development and its operating mechanism. The results show that the levels of the digital economy and regional sustainable development are rising but with significant spatial distribution differences, displaying a pattern of “high in the east and low in the west.” The digital economy significantly enhances the level of regional sustainable development, which remains valid after a series of robustness tests. Increasing marketization levels, reducing labor mismatches, and adjusting labor structures are important mechanisms through which the digital economy promotes regional sustainable development. There is regional heterogeneity and a nonlinear relationship in the impact of the digital economy on regional sustainable development. This study provides an empirical analysis of the digital economy’s influence on regional sustainable development and its underlying mechanisms, which are congruent with the advancement of the local knowledge economy. The intelligent technologies facilitated by the digital economy not only reshape the developmental trajectories of traditional industries at the local level but also foster an environment conducive to knowledge creation, dissemination, and collaboration. This accelerates the process of regional sustainable development.

PurposeBuilding upon the resource-based view (RBV) and related research, this paper empirically examines the impact and specific mechanisms of artificial intelligence transformation on corporate innovation capabilities. It provides micro-level evidence of AI’s influence on innovation behavior.Design/methodology/approachDrawing upon data from Chinese listed companies spanning the period from 2011 to 2022, this study employs a dual fixed-effects model and a mediation effects model to empirically analyze the influence of enterprise AI transformation on its innovation capability as well as the specific mechanisms involved.FindingsThe research reveals that AI transformation significantly enhances the innovation capability of enterprises. Heterogeneity analysis indicates that AI transformation exerts a stronger promoting effect on the innovation capability of non-technology firms, large enterprises and those within the manufacturing sector. Mechanism analysis further reveals that AI transformation enhances innovation capability by boosting enterprise profits, reducing costs and reinforcing internal control mechanisms. Further examination demonstrates that AI transformation elevates the quality, efficiency and eco-friendliness of enterprise innovation.Originality/valueFirstly, this study employs text analysis methods from machine learning to construct artificial intelligence indicators at the firm level, providing stronger evidence of AI’s impact on corporate innovation capabilities. Secondly, it extends corporate innovation behavior to include innovation quality, efficiency and green innovation practices, offering a more comprehensive validation of AI’s role in fostering corporate innovation.

As an important economic growth pole and ecological area in China, the urban agglomeration of the Yangtze River Economic Belt (YREB) is the key to carbon emission reduction. Exploring the spatial-temporal evolution and driving variables of its carbon emission efficiency (CEE) is crucial for realizing the goals of carbon peaking and carbon neutrality. The super-efficiency SBM model, the nuclear density method, and the spatial autocorrelation method were used to discuss the spatial-temporal CEE characteristics of 105 cities in the YREB. On the driving factors of carbon emissions, the geographic detector and the Tobit model were combined to explore the spatial differentiation characteristics of the driving factors from the perspective of heterogeneity, and concurrently analyze the single-factor's effecting intensity and impacting direction, as well as the dual-factors' interaction effects. The findings indicated that the CEE of YREB generally showed a slow upward trend during 2006-2021. From the perspective of time dynamic evolution, the differentiation of efficiency intensified, and the overall development was toward the high efficiency level. Furthermore, the results of spatial pattern evolution showed that the CEE presents the pattern of "downstream areas > midstream areas > upstream areas", "high in the east and low in the west", "hot in the east and cold in the west", while the spatial clustering effect was significant, showing the distributions of low-low clustering or high-high clustering. Moreover, the results of the geographic detector showed that government intervention, economic growth, and technological progress were the main driving factors. In addition, the interactions government intervention and the other factors were significantly detected. Tobit regression results showed that technological advancement and economic growth had a favorable impact on CEE, but foreign investment, urbanization, and government involvement had negative impacts. In the future, the correlations between provinces and cities should be strengthened and amplified to promote the integrated green development, as well as to improve the ecological environments.

This study comprehensively assessed carbon dioxide emissions over a span of two decades, from 2000 to 2020, with the decomposition and decoupling analyses considering multiple influence factors across both short-term and long-term dimensions. The results revealed great fluctuations in the decoupling analysis index (DAI) for subjected sectors such as natural resource processing, electricity, gas, water, textiles, machinery, and electronics manufacturing. Of note, significantly changed sectoral DAIs were observed in urban traffic and transportation, logistics warehousing, and the postal industry within Anhui Province. In contrast, the DAIs of other sectors and social services exhibited a weak decoupling state in Anhui Province. The industrial sectors responsible for mining and textiles and the energy structure encompassing electricity, gas, and water emerged as the primary contributors to carbon dioxide emissions. Additionally, the efficiency of the socio-economic development (EDE) was identified as the principal driver of carbon dioxide emissions during the observed period, while the energy consumption intensity (ECI) served as the putative crucial inhibiting factor. The two-dimensional decoupling of carbon dioxide emissions attributable to the EDE demonstrated a gradual transition from industrial sectors to buildings and tertiary industries from 2000 to 2020. In the future, the interaction between urban carbon dioxide emissions and the socio-economic landscape should be optimized to foster integrated social sustainable development in Anhui Province.

When the process of urbanization has brought economic benefits in Yangtze River Economic Belt (YREB), the environmental issues become increasingly prominent. The evolutionary coupled coordination degree (CCD) between urban social economy and ecological environments was explored in 41 cities of YREB. Results indicated that the overall CCD between urban social economy and ecological environments showed a fluctuating upward trend from 2010 to 2020 in YREB. The CCD of Shanghai was the best among these of 41 cities, while those of three provinces were lower than the average CCD of YREB. There were significant differentiated spatial clusters of high and low among these YREB cities in both the global and local spatial autocorrelation analyses during 2010-2020. The CCD estimations indicated a fitted spatial distribution of "high in the east and low in the west" in YREB. Furthermore, CCD was positively influenced by the urban density of population, economic acceleration and technical advancement (P<0.01), while it was negatively influenced by the urban industrial structures and energy consumption (P<0.01). Subsequently, corresponding policy implications and recommendations, including enhancing policy innovation and promoting technological progress, were proposed to facilitate the formulation of evidence-based developmental strategies and policies of sustainable cities and society in YREB.

2016 年 11 月,安徽省人民政府颁发关于深入推进新型城镇化试点省建设方案和实施意见,以期提升全省新型城镇化的质量和水平.本研究基于 2007-2016 和 2017-2020 两个时间段安徽省 59 个县市在经济社会可持续发展潜力方面的时间序列数据集,构建了一套综合评价指标体系,运用因子分析法比较了安徽省七个县改市在全省 59 个县市的综合比较中可持续发展潜力的高低及其变化发展趋势.研究结果表明:安徽省 59 个县市的可持续发展潜力总体水平不高,但差异显著;安徽省七个县改市的可持续发展潜力总体上高于各县市的平均水平;县改市的可持续发展潜力和发展水平处于中等偏上.据此,提出了一些相关的对策和建议.

In order to further improve the development level of common prosperity in Zhejiang Province, a high-quality development system with common prosperity index is constructed from such five dimensions as balanced urban and rural development, regional science and education development, medical, cultural and social security, financial and people’s livelihood and regional industrial development structure. The entropy method is used to estimate the high-quality development level of Zhejiang Province from 2015 to 2020 and with the coupling coordination model to explore the level of coordination development. The results show that the high quality development level of Zhejiang Province is rising steadily, and all the cities maintain a good development trend, among which Hangzhou City, Ningbo City and Shaoxing City are in the forefront; Zhejiang Province as a whole is in the moderate coordination stage and the coordination degree increases gradually with the time. Hangzhou, Ningbo and Shaoxing are at the forefront of the coordination level. It is suggested that the government continue to improve the level of development coordination among different cities and promote the coordinated development of Zhejiang Province.

基于知识图谱理论,运用CiteSpace软件对中国知网中2003-2021年以国内碳排放为主题的核心期刊文献进行可视化分析.结果如下:国内碳排放研究文献发表总体呈现上升—下降—上升的发展趋势;国内碳排放研究热点集中于碳排放、低碳经济、碳排放权、经济增长等,早期研究集中于碳排放权交易市场现状、体系构建等,后期研究集中于碳排放效率评价研究、碳排放量预测研究、碳减排路径研究等.未来可着眼于微观层面的碳排放研究、碳库及碳汇系统的研究、碳减排政策效果评估研究等.

基于安徽省2014—2019年发展数据,从区域、市域和规模以上工业企业3个角度对安徽省科技发展水平进行分析,并通过全局主成分分析法对安徽各市科技发展水平进行排名,运用DEA模型和Malmquist指数对各市规模以上工业企业科技投入效率进行静态和动态测算,研究发现:安徽省在科技投入和产出上与长三角其他地区有较大的差距;科技资源投入较为集中,主要集中在合肥、芜湖、蚌埠、马鞍山和滁州;地区规模以上工业企业效率总体有效,技术变化成为企业科技效率提高的主要制约因素.因此建议政府优化资源配置,协调地区科技发展水平.

CYP-mediated fast metabolism may lead to poor bioavailability, fast drug clearance and significant drug interaction. Thus, metabolic stability screening in human liver microsomes (HLM) followed by metabolic soft-spot identification (MSSID) is routinely conducted in drug discovery. Liver microsomal incubations of testing compounds with fixed single or multiple incubation time(s) and quantitative and qualitative analysis of metabolites using high-resolution mass spectrometry are routinely employed in MSSID assays. The major objective of this study was to develop and validate a simple, effective, and high-throughput assay for determining metabolic soft-spots of testing compounds in liver microsomes using a single variable incubation time and LC/UV/MS. Model compounds (verapamil, dextromethorphan, buspirone, mirtazapine, saquinavir, midazolam, amodiaquine) were incubated at 3 or 5 µM with HLM for a single variable incubation time between 1 and 60 min based on predetermined metabolic stability data. As a result, disappearances of the parents were around 20–40%, and only one or a few primary metabolites were generated as major metabolite(s) without notable formation of secondary metabolites. The unique metabolite profiles generated from the optimal incubation conditions enabled LC/UV to perform direct quantitative estimation for identifying major metabolites. Consequently, structural characterization by LC/MS focused on one or a few major primary metabolite(s) rather than many metabolites including secondary metabolites. Furthermore, generic data-dependent acquisition methods were utilized to enable Q-TOF and Qtrap to continuously record full MS and MS/MS spectral data of major metabolites for post-acquisition data-mining and interpretation. Results from analyzing metabolic soft-spots of the seven model compounds demonstrated that the novel MSSID assay can substantially simplify metabolic soft-spot identification and is well suited for high-throughput analysis in lead optimization.

Global warming and world-wide climate change caused by increasing carbon emissions have attracted a widespread public attention, while anthropogenic activities account for most of these problems generated in the social economy. In order to comprehensively measure the levels of carbon emissions and carbon sinks in Anhui Province, the study adopted some specific carbon accounting methods to analyze and explore datasets from the following suggested five carbon emission sources of energy consumption, food consumption, cultivated land, ruminants and waste, and three carbon sink sources of forest, grassland and crops to compile the carbon emission inventory in Anhui Province. Based on the compiled carbon emission inventory, carbon emissions and carbon sink capacity were calculated from 2000 to 2019 in Anhui Province, China. Combined with ridge regression and scenario analysis, the STIRPAT model was used to evaluate and predict the regional carbon emission from 2020 to 2040 to explore the provincial low-carbon development pathways, and carbon emissions of various industrial sectors were systematically compared and analyzed. Results showed that carbon emissions increased rapidly from 2000 to 2019 and regional energy consumption was the primary source of carbon emissions in Anhui Province. There were significant differences found in the increasing carbon emissions among various industries. The consumption proportion of coal in the provincial energy consumption continued to decline, while the consumption of oil and electricity proceeded to increase. Furthermore, there were significant differences among different urban and rural energy structures, and the carbon emissions from waste incineration were increasing. Additionally, there is an inverted “U”-shape curve of correlation between carbon emission and economic development in line with the environmental Kuznets curve, whereas it indicated a “positive U”-shaped curve of correlation between carbon emission and urbanization rate. The local government should strengthen environmental governance, actively promote industrial transformation, and increase the proportion of clean energy in the energy production and consumption structures in Anhui Province. These also suggested a great potential of emission reduction with carbon sink in Anhui Province.

结合安徽省不同地区的产业结构和发展情况,采用因子分析法对2010—2019年安徽省影响农民收入的相关因素进行分析,通过回归模型分析各因素对农民收入的影响程度.结果发现:农民收入水平变化存在正向空间自相关,在空间上形成高高集聚和低低集聚的分布特征;地区经济发展因子和第三产业发展因子对农民收入起到正向作用,而农业机械发展均衡因子、当地基建和农业支持因子与农民收入的关系并不显著;城镇化率的提高和第三产业中的服务业对增加农民收入有积极促进作用.据此提出缩小地区间农民收入差距的建议:以地区带动的方式改变区域间经济发展的不平衡;构建完善的就业培训体系,推动乡村服务业加快发展;以第三产业带动城镇化水平提高.

When the process of urbanization has brought economic benefits in the Yangtze River Delta of China, environmental pollution becomes increasingly prominent. In order to achieve integrated sustainable green development and reduce the gap in environmental governance performance between regions, this study analyzed the environmental issues of provincial cities in Anhui Province from 2013 to 2017 in the urban agglomeration of Yangtze River Delta. Governance performance is analyzed and the evaluation index system framework is determined using the "pressure-state-response" model with the panel and spatial data. Based on the global principal component analysis method and spatial autocorrelation analysis, the environmental governance performance of Anhui Province has generally increased steadily from 2013 to 2017. The situation in northern Anhui is still developing in a good state. Southern Anhui is in a trend of rising first and then stabilizing, whereas central Anhui has a downward trend after a rapid rise; in terms of the spatial pattern, the overall situation is central Anhui > northern Anhui > southern Anhui. The urban spatial distribution pattern of the region shows a positive spatial correlation. Particularly, the performance levels of Maanshan City and Huainan City have been at a poor level for a long time, whereas Hefei and Huangshan have strong comprehensive environmental governance capabilities with average efficiency values of 0.55 and 0.47, respectively. Corresponding countermeasures have been proposed to rectify polluting enterprises and optimize structure of industries, increase scientific and technological investment and infrastructure construction, strengthen the radiation driving effects, and establish a pollution monitoring system. Based on all the analyses and resulted findings, we concluded the study with corresponding policy implications/suggestions and recommended countermeasures.

以长三角地区2014—2019年发展数据为基础,以新发展理念为指导,构建长三角地区高质量发展水平评价指标体系.采用熵权法对长三角地区高质量发展水平进行评价,结合耦合协调度模型对各地区5大维度综合发展协调程度进行分析.结果表明:上海市发展指数最高,其后依次为江苏省、浙江省、安徽省;在创新发展层面,上海发展最好,安徽最差;在绿色发展层面,上海市平稳上升,浙江省水平较低;上海和江苏开放和共享发展较好,安徽省产业协调发展较好.整体发展协调度大小依次为上海市、江苏省、浙江省、安徽省.

As the development of Yangtze River Delta urban agglomeration has been upgraded to a national level development strategy in China, relevant regions need to pay more attention to the environmental governance issues. Based on the 2013-2018 development data of 41 cities and the established evaluation indicator system with "press-state-response" (PSR) model, we mainly use a combination of global principal component analysis (GPCA) and entropy method to comprehensively measure regional environmental governance performance (EGP) in the Yangtze River Delta urban agglomeration. Next, the spatial relationship is explored and discussed with spatial autocorrelation analysis. Finally, the panel Tobit model is used to perform a regression analysis on the factors affecting the performance of environmental governance in the Yangtze River Delta region. The results are summarized as follows. (1) From 2013 to 2018, the overall environmental governance performance of the Yangtze River Delta region maintained a steady growth trend, of which the environmental governance performance of Jiangsu Province and Shanghai maintained a steady increase, and the environmental governance performance of Anhui and Zhejiang Province fluctuated greatly. (2) These cities with better EGP are mainly located in the central and southern regions of the Yangtze River Delta, and those cities with poor EGP are mainly located in the northern part of the Yangtze River Delta. The environmental governance performance gap between provinces and cities is obviously large, and there are significant spatial positive correlations, spatial spillover effects and a trend of agglomeration with increased volatility. (3) The regression analysis shows that the economic development level has a significant positive impact on the EGP of the Yangtze River Delta region. Meanwhile, the negative impact of industrial structure and foreign investment is significant, but the positive impact of R&D investment intensity is insignificant, on the performance of environmental governance in the Yangtze River Delta region.