Cities Move Towards Green Sustainable Development: A Perspective Based on Artificial Intelligence Policy

Digital transformations, particularly in artificial intelligence (AI), compel economies to adapt to new competitive conditions and market demands. Ukraine has made strides in AI development, influenced by both national initiatives and international principles. The research aims to evaluate the state of artificial intelligence (AI) policy in Ukraine from 2020 to 2022 and its normative impact on the restructuring of the economy. The evaluation utilized the methodology developed by the Center for AI and Digital Policy. These indicators range from the endorsement and implementation of OECD AI Principles to the establishment of a process for meaningful public participation in AI policy development. The matrix method is applied to simulate the normative impact of AI policy in Ukraine. The analysis focuses on several key aspects of Ukraine's AI policy, including the endorsement of the National AI Strategy, public participation initiatives, and significant AI developments. The overall evaluation score for AI policy in Ukraine from 2020 to 2022 is 6.5, indicating that Ukraine's AI policy is still in its formative stages and primarily focuses on aligning with international standards and practices. The matrix for assessing the normative impact of AI Policy on the restructuring of Ukraine's national economy is formed. Evaluation of normative impact of AI Policy on the restructuring of Ukraine's national economy is done. The influence of AI policy on the restructuring of Ukraine's national economy is minor. Both the normative-legal and infrastructural components of AI policy's impact on the restructuring of Ukraine's national economy should be enhanced through the adoption of international norms and the development of infrastructure facilities. Additionally, the motivational aspect of AI policy's influence on the restructuring of Ukraine's national economy necessitates the creation of programs aimed at motivating economic entities to integrate AI technologies.

The green effect of digital transformation: The impact of digital transformation in fiscal and taxation on regional green development

As a high-quality and sustainable growth model, green development has different economic, ecological, and social dimensions and is strategically important for the realization of modern city construction and the sustainable development of human society. The low-carbon city pilot policy (LCCP) is an innovative initiative for promoting green urban development and building a harmonious society in China. Based on balanced panel data from 277 prefecture-level cities from 2007 to 2020, this paper measures the level of urban green development in terms of three dimensions: green economic growth, ecological welfare enhancement, and social welfare increase. This paper also adopts a multi-period difference-in-differences (DID) method for investigating the impact of LCCP on green development with the panel dataset. The results of the study show that: (1) LCCP is generally beneficial to urban green development, and the results still hold after a series of robustness check analyses. (2) The results of the mechanism analysis show that the construction of low-carbon cities has improved the level of green technology innovation, thereby promoting the level of regional green development. Environmental regulation has a masking effect between low-carbon city construction and green development in this study. When environmental regulation is controlled for, the coefficient of the effect of LCCP on green development increases, reflecting that environmental regulation also plays an important role between the two. (3) According to the geographical location, whether it is a resource-based city, and the city cluster, we found that the low-carbon city pilot policy has a significant positive role in promoting green development in the central region, non-resource-based cities, and the Jing-Jin-Ji, but not in the eastern region, the western region, the Yangtze River Delta and Pearl River Delta. We also found that in resource-based cities, this effect presents a significant negative relationship. The above findings enrich the literature on low-carbon city pilot policies and green development and provide Empirical evidence for relevant countries and regions to carry out low-carbon city pilots.

Spatial correlation network characteristics and influencing factors of water environmental efficiency in three major urban agglomerations in the Yangtze River Basin, China

Building efficiency: How the national AI innovation pilot zones enhance green energy utilization? Evidence from China.

Introduction: Behind China’s booming economy lies a series of environmental and resource consumption issues. After continuous research and exploration, scholars generally agree that green innovation is a crucial way to solve this problem. As the core regions of China’s economic development, studying the green innovation level of the three major urban agglomerations can help understand China’s progress in this area. It can provide beneficial experience and inspiration for other urban agglomerations and the formulation of relevant policies in the future.Methods: This paper selects the panel data of 40 cities in Beijing-Tianjin-Hebei (BTH), Yangtze River Delta (YRD), and Pearl River Delta (PRD) urban agglomerations from 2010 to 2020 as the research object. The super-efficiency SBM (slack-based measure) model and Malmquist-Luenberger (ML) index are used to measure the green innovation efficiency (GIE) and its dynamic evolution rules, and the Tobit regression model is constructed to analyze the influencing factors of GIE.Results: The PRD urban agglomeration has the highest GIE level, while the GIE level in BTH and YRD shifted around 2015. Technical efficiency and technological progress together lead to increased GIE, with technological progress having a higher impact than technical efficiency. The regression coefficients of urbanization level, industrial structure, and science and technology level are 0.0078, 0.0071, and 0.0616, respectively, significantly promoting GIE. The coefficients of economic development level, foreign direct investment, environmental regulations, and SO2 emissions are −0.2198, −0.1163, −0.005, and −0.011, respectively, significantly inhibiting GIE. The coefficient of vegetation cover of 0.0228 has no significant effect on GIE.Conclusions: The overall GIE of the three major urban agglomerations is relatively high. Still, there is spatial variability in GIE among different cities, accompanied by the phenomenon of two-level differentiation. The study suggests that improving GIE requires enhanced interventions at both the city level and the level of influencing factors. This study enriches the theoretical results on the meso-level of GIE and provides theoretical guidance and practical directions for promoting green innovation in urban agglomerations, achieving peaking carbon and carbon neutrality, and promoting green and high-quality development.

BackgroundChina has been exploring a sustainable development path that harmonizes economic growth and environmental protection, targeting to build a beautiful China. The role of green finance in adjusting the misallocation of financial resources and leading the green sustainable development of the real economy is receiving increasingly more attention. Currently, green credit accounts for more than 90% of the total green finance funding in China and constitutes the most significant component of the green finance matrix. Whether green credit effectively promotes the green and sustainable development of the regional economy largely determines the success of China’s economic green transformation.ObjectiveExisting studies of green credit mainly focus on its influences on financing, investment, and emission reduction of environmental pollution industries or companies. Extending the literature by exploring whether green credit is effective in promoting green sustainable development and what impact green credit exerts on the upstream (energy inputs), midstream (technological innovation), and downstream (pollution outputs) stages of the green sustainable development value chain, is the leading research objective of this paper.MethodsThis paper discusses the impact of green credit on green sustainable development based on city panel data from 2012 to 2019. The level of green sustainable development is calculated by the GML index based on SBM directional distance function. The city-level green credit scale is calculated from the green credit issued by banks, weighted by the density of bank branches in a city. Synthetic control methods are employed in the robustness analysis to reduce the impact of endogeneity issues.Results and conclusionThe results of this paper indicate that green credit can promote green sustainable development and the impact gradually strengthens over time as the incremental implementations of complementary policies with substantial constraints and incentives, through which pollution control and economic growth achieve a "win-win" situation. Furthermore, the results indicate that green credit reduces the overall amount of energy inputs while optimizing the energy input structure. However, green credit does not boost the green technological level and even crowds out high technical value green innovations. Besides, the pollution reduction effects of green credit are associated with the strength of green credit constraints and the importance of pollution industries in the local economy, which means green credit performs better pollution reduction effects in regions with relatively strong green credit binding effects or in regions where pollution industries are not local economic pillars. The empirical results are further validated through robustness tests, including changing scope and measurement variables and applying the synthetic control method.LimitationsAlthough this paper provides valuable contributions to the research area of green credit and green sustainable development, specific limitations exist in the current study. Firstly, as the official information disclosure of green credit in China is not sufficient, existing studies, including ours, could only use estimation methods through different perspectives to measure green credit, which is overall logical and reasonable but may lose some accuracy. Secondly, since there might be a certain degree of lag in the effect of green credit on the economy, the dynamic impact and long-term effects of green credit deserve further study. Thirdly, considering the characteristics of the Chinese administrative systems, introducing the behavior of local governments and local officials into the analysis of green credit and green sustainable development could be valuable.

As artificial intelligence (AI) further embeds itself into many settings across personal and professional contexts, increasing attention must be paid not only to AI ethics, but also to the governance and regulation of AI technologies through AI policy. However, the prevailing post-secondary computing curriculum is currently ill-equipped to prepare future AI practitioners to confront increasing demands to implement abstract ethical principles and normative policy preferences into the design and development of AI systems. We believe that familiarity with the ‘AI policy landscape’ and the ability to translate ethical principles to practices will in the future constitute an important responsibility for even the most technically-focused AI engineers. Toward preparing current computer science (CS) students for these new expectations, we developed an AI Policy Module to introduce discussions of AI policy into the CS curriculum. Building on a successful pilot in fall 2024, in this innovative practice full paper we present an updated and expanded version of the module, including a technical assignment on “AI regulation”. We present the findings from our pilot of the AI Policy Module 2.0, evaluating student attitudes towards AI ethics and policy through pre- and post-module surveys. Following the module, students reported increased concern about the ethical impacts of AI technologies while also expressing greater confidence in their abilities to engage in discussions about AI regulation. Finally, we highlight the AI Regulation Assignment as an effective and engaging tool for exploring the limits of AI alignment and emphasizing the role of ‘policy’ in addressing ethical challenges.

In the context of climate change, studying the ecological efficiency (EE) of urban agglomerations is of great significance in promoting sustainable development. First, night light data are used as the expected output to build an evaluation index system based on the five major urban agglomerations, namely, the Yangtze River Delta, Pearl River Delta, Beijing–Tianjin–Hebei, the middle reaches of the Yangtze River, and Chengdu–Chongqing urban agglomerations. Second, the super-efficient Epsilon-based (super-EBM) model and the input–output redundancy rates are used to measure the EE of the five major urban agglomerations from 2006 to 2018. Then, their spatial differences are explored with the help of the Gini coefficient. Finally, the spatial differences in the EE drivers of urban agglomerations are analyzed using Geodetector. The results reveal the following. (1) The EE of the five major urban agglomerations present the decline fluctuation trend of “∧”. However, this trend has slowed down. From the perspective of urban agglomeration, Beijing–Tianjin–Hebei > The Pearl River Delta > Chengdu–Chongqing > Yangtze River Delta > the middle reaches of the Yangtze River. The lowest efficiency of the Yangtze River’s middle reaches has “high investment, low output, and high pollution” characteristics. (2) The EE of the five major urban agglomerations had weak synergistic development and noticeable spatial differences. The primary sources are inter-group differences and hypervariable density. (3) From the perspective of influencing, the difference in technological innovation levels (TEC) is the single leading factor in the differences in the EE space of urban agglomerations. In addition, the interaction combination of industrial structure upgrades (IDS) and traffic infrastructure (TRAF) is a crucial combination driver. However, the core influencing factors of spatial differences in EE in five urban agglomerations are heterogeneous. Among them, the nature-influencing factors of the EE space differences in the Beijing–Tianjin–Hebei and the Chengdu–Chongqing urban agglomerations are environmental regulations (ER). Meanwhile, the influencing factor in the Yangtze River Delta urban agglomeration is the development of urbanization (URB). Moreover, the prominent factor in the middle reaches of the Yangtze River and the Pearl River Delta urban agglomerations is foreign direct investment (FDI). On this basis, this study aims to promote ecological civilization construction in urban agglomerations and optimize regional integrated spatial patterns.

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development.

On the background of climate change, studying tourism eco-efficiency of cities is of great significance to promote the green development of tourism. Based on the panel data of the three major urban agglomerations in China’s Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei region from 2008 to 2017, this paper constructed an evaluation index system and measured the tourism eco-efficiency of 63 cities by using a hybrid distance model called Super-EBM (epsilon-based measure). We compared the spatial and temporal evolution characteristics of tourism eco-efficiency in the three urban agglomerations. Furthermore, the internal factors influencing tourism eco-efficiency were explored through input–output redundancy, and the external factors were analyzed by a panel regression model. The results indicate that the tourism eco-efficiency of the three urban agglomerations in China generally shows a decreasing-rising-declining trend. Among them, the Yangtze River Delta has the highest eco-efficiency, followed by the Pearl River Delta, and the lowest in the Beijing–Tianjin–Hebei region. Moreover, there is a certain gap within each urban agglomeration. The redundancy input of labor and capital is the main internal cause of low eco-efficiency. Among the external factors, the status of the tourism industry and the level of urbanization have a positive effect on eco-efficiency, while the level of tourism development, technological innovation and investment have a negative impact on it. In the future, we must attach great importance to the development quality and overall benefit value of the tourism industry so as to achieve green and balanced development of the three major urban agglomerations in eastern China. Based on the above conclusions, this paper puts forward targeted policy implications to improve the tourism eco-efficiency of cities.

Faced with the dual challenges of ecological degradation and economic deceleration, promoting urban green high-quality development (UGHQD) is pivotal for achieving economic transformation, ecological restoration, and regional sustainable development. While the existing literature has delved into the theoretical dimensions of UGHQD, there remains a notable dearth of empirical studies that quantitatively assess its developmental levels, spatio-temporal evolution, and driving factors. This study examines 107 cities of China’s five major urban agglomerations from 2003 to 2020, constructing a comprehensive evaluation indicator system for UGHQD. By employing methodologies, including the Dagum Gini coefficient, Kernel density estimation, Markov chain, and geographical detector, this study extensively assesses the spatial difference, dynamic evolution, and underlying driving forces of UGHQD in these urban agglomerations. The findings indicate: (1) The UGHQD level of the five major urban agglomerations has witnessed a consistent year-over-year growth trend, with coastal agglomerations like the Pearl River Delta (PRD) and Yangtze River Delta (YRD) outperforming others. (2) Pronounced regional differences exist in UGHQD levels across the urban agglomerations, with inter-regional differences primarily contributing to these differences. (3) The dynamic evolution of UGHQD distribution generally transitions from a centralized to a decentralized pattern, with a marked “club convergence” characteristic hindering cross-type leaps. (4) While a range of factors drive UGHQD in these agglomerations, technological innovation stands out as the principal factor inducing spatial differentiation. The comprehensive analysis and findings presented in this research not only contribute to academic knowledge but also hold practical implications for policymakers and practitioners striving for environmentally conscious land use planning and urban management.

ITA and Beast methods were used to quantitatively analyze the nonlinear process of a PM2.5 concentration time series based on the PM2.5 concentration data of the three major urban agglomerations in China. The results showed that： ① the degree of the PM2.5 pollution in the three major urban agglomerations had decreased， and the high-concentration areas had noticeably shrunk. The degree of spatial polarization of PM2.5 concentration was reduced， and the spatial difference was narrowed. The PM2.5 concentration in most areas showed downward trends， but the degree of change was not the same. Compared with the YRD and PRD， the concentration of PM2.5 in the BTH was still at a relatively high level. ② The concentration of PM2.5 in the three major urban agglomerations had seasonal variation characteristics that were high in winter and spring and low in summer and autumn. There were obvious differences in PM2.5 concentration between winter and summer， and the convergence of PM2.5 concentration in summer was greater than that in winter. Areas with high PM2.5 concentration also had obvious downward trends， but the downward trends of PM2.5 concentration in the PRD were not obvious compared with those in the YRD and BTH. ③ The PM2.5 concentration time series of the three major urban agglomerations all had significant downward trends： Beijing-Tianjin-Hebei （BTH） > the Yangtze River Delta （YRD） > the Pearl River Delta （PRD）. The PM2.5 concentration had the largest downward trends in winter. The higher the PM2.5 pollution level， the greater the downward trends. ④ The trend component of the PM2.5 concentration time series in the BTH had two change points， and there was one change point in the seasonal component. The trend and seasonal components of the PM2.5 concentration time series in the YRD had no change point. There was no change point in the seasonal component but one change point in the trend component of the PM2.5 concentration time series in the PRD. These results can provide scientific references for regional air pollution control.

Based on the data of 85 cities in the three major urban agglomerations in the Yangtze River Economic Belt from 2011 to 2020, this paper measures the level of high-quality economic development of each city through the improved entropy-weighted TOPSIS method and uses the kernel density estimation method, Dagum Gini coefficient, and convergence model to further study the dynamic evolution, regional differences, and convergence state of the high-quality economic development level of the three major urban agglomerations in the Yangtze River Economic Belt. The study finds that: 1) the high-quality economic development level of each city within the three major urban agglomerations of the Yangtze River Economic Belt has been continuously improved over time, but the development level of each urban agglomeration is not the same; 2) the high-quality economic development level of the three urban agglomerations in the Yangtze River Economic Belt has spatial differences, that is, the high-quality economic development level of the Yangtze River Delta urban agglomeration is better than the two urban agglomerations; 3) the σ convergence and β convergence characteristics exist in the three major urban agglomerations as a whole as well as in each urban agglomeration, and the β convergence rates of the urban agglomerations are not consistent. On the whole, the convergence rate of the middle reaches of the Yangtze River urban agglomeration is the fastest, followed by the Chengdu-Chongqing urban agglomeration, and the Yangtze River Delta urban agglomeration is the slowest. Finally, based on the findings of the study, targeted policy recommendations to promote the high-quality economic development of the three major urban agglomerations in the Yangtze River Economic Belt are proposed.

In a developing country such as Chile, the use of both functional and social robots to battle and surmount the obstacles posed by the COVID-19 pandemic is still rather isolated. In part, this is due to the novelty of the pandemic, but also due to the country's limited development of robotic initiatives. The present chapter presents and focuses on two recent robotic pilot-initiatives, deployed in some of the poorest areas of the country: flying drones to bring medicine and preventive care supplies to risk populations; and Eva, a health care robot which can check up on patients and enable social connections through telepresence. In the midst of this, in 2019 Chile started working in the creation of its future national artificial intelligence (AI) policy, a process which has faced unexpected delays, such as the Chilean social unrest in October of that same year, and most recently, the COVID-19 outbreak. While robots hold interesting promises with regards to preventing infection, executing sanitation duties, and social connectedness, the current robotics initiatives in the country are still sparse and are faced by some ethical challenges. In addition, robotics does not appear to be one key area to develop as described in the main three areas of focus of Chile's AI policy, although groups from different sectors in society are working towards defining and presenting key issues. The chapter concludes by shedding light into the process of AI policy creation, with which the country is incipiently paving its way towards a greater development in AI – of which robotics could become an important element in the future.