Search Results (63)

The transition to a low-carbon economy is a key challenge for Eastern Europe, where economic growth, energy investments, and emission reduction policies interact in complex ways. This study employs a spatial econometric approach to assess the effectiveness of renewable energy investments and government environmental spending in mitigating CO₂ emissions across the region. Using panel data and spatial Durbin models (SDMs), we identify significant spillover effects in emissions reduction, revealing those environmental policies in one country influence neighboring regions. The results indicate that renewable investments have a positive but localized impact on emissions reduction, whereas government environmental expenditure exhibits diminishing returns beyond a threshold of 0.01 GDP. Threshold regression analysis confirms that excessive spending may lead to inefficiencies, reversing its expected benefits. Additionally, stochastic frontier analysis (SFA) highlights disparities in energy efficiency, with some countries demonstrating stronger optimization strategies than others. These findings underscore the importance of policy coordination and targeted investment strategies to enhance the effectiveness of decarbonization efforts. Strengthening regional cooperation and optimizing environmental expenditure allocation can significantly improve sustainability outcomes in Eastern Europe. Full article

Ecological carrying capacity (ECC) is a crucial indicator for assessing sustainable development capabilities. However, mountain ecosystems possess unique complexities due to their diverse topography, high biodiversity, and fragile ecological environments. Addressing the current shortcomings in mountain ECC assessments, this paper proposes a novel hybrid evaluation framework that integrates improved ecological footprint (EF) and ecosystem service value (ESV) approaches with spatial econometric models. This framework allows for a more comprehensive understanding of the dynamic changes and driving factors of the mountain ecological carrying capacity index (ECCI), using Pingbian County as a case study. The results indicate the following: (1) Land use changes and biodiversity exert varying impacts on the ECCI across different regions. The ECCI decreased by 42% from 2003 to 2021 (from 4.41 to 2.54), exhibiting significant spatial autocorrelation and heterogeneity. (2) The ecological service value coefficient is the main factor increasing the ECCI, while the energy consumption value and per capita consumption value inhibited the increase in the ECCI. For every 1% increase in the ecosystem service value coefficient, the ECCI increased by 0.66%, whereas every 1% increase in energy consumption value and per capita consumption value reduced the ECCI by 0.18% and 0.28%, respectively. (3) The overall spatial distribution pattern of the ECCI is primarily “southwest to northeast”, with the distance of centroid migration expanding over time. Based on these key findings, implementing differentiated land use practices and ecological restoration measures can effectively enhance the mountain ECCI, providing scientific support for the sustainable management of mountain areas. Full article

Variable selection methods have been a focus in the context of econometrics and statistics literature. In this paper, we consider additive spatial autoregressive model with high-dimensional covariates. Instead of adopting the traditional regularization approaches, we offer a novel multi-step sparse boosting algorithm to conduct model-based prediction and variable selection. One main advantage of this new method is that we do not need to perform the time-consuming selection of tuning parameters. Extensive numerical examples illustrate the advantage of the proposed methodology. An application of Boston housing price data is further provided to demonstrate the proposed methodology. Full article

Digitization plays a vital role in fostering economic and social development. This study empirically investigates the impact of digitization on urban industrial structures, technological innovation, public service levels, and social–ecological resilience. Various approaches, including the two-tier stochastic, spatial econometric, and panel threshold models, have been employed to analyze panel data from 287 cities from 2008 to 2023. These data are examined through a quasi-natural experiment analyzing the evolution of urban social–ecological resilience following China’s promotion of the national comprehensive pilot zone for big data. The findings are as follows. (1) The positive effects of digitization on urban social and ecological resilience substantially outweigh the negative effects, with an overall increasing trend in the positive net effect, albeit with significant regional differences. (2) Digitalization exhibits a significant spatial spillover effect, enhancing local social–ecological resilience while inhibiting improvements in neighboring cities. (3) Technological innovation and public service levels positively affect social–ecological resilience, whereas industrial structure upgrading has a negative indirect effect. Both industrial structure upgrading and public service levels demonstrate nonlinear effects under the threshold constraints of the intermediary mechanism. (4) In terms of policy mechanisms, regional differences in the urban industrial structure, innovation capacity, and public service levels must be considered. This approach is essential for promoting the organic integration of digitization across regions, mitigating the polarization effect, and enhancing the diffusion effect. Full article

The new quality productive forces have the potential to spur both the green transformation of the industrial structure and innovative advances in green technology, which will further strengthen the foundation for sustainable growth. This study analyzes panel data from 30 provinces between 2012 and 2022 to build an evaluation system for new quality productive forces and green development at the provincial level. The entropy weight TOPSIS approach is used to assign weights to each indicator. Methods including fixed effects, mediation effects, and spatial econometrics are used to examine the contribution of new quality productive forces to green development and its mediation mechanism. The study finds that: (1) New quality productive forces significantly promote green development, and the conclusion still holds after a robustness test using the instrumental variables method and excluding municipalities. (2) The new quality productive forces contribute significantly to green development by improving technology and optimizing industrial structure. (3) The new quality productive forces not only directly enhance the green development level of the region, but also positively influence the green development level of the neighboring regions through the spatial spillover effect. (4) The eastern and central regions are more affected by new productivity in terms of green development. Based on these, efforts should be made to develop new quality productive forces, increase technological research and investment, and promote the development of industrial structure to be more environmentally friendly and efficient to promote green development. Full article

This study elaborates on a typology of demographic change and tests this definition at the lowest granular level (LAU2, municipality) with official data. This typology distinguishes between fragile and resilient municipalities based on population dynamics (in terms of duration and intensity) over 1991–2021. This study’s second aim is to elaborate a spatial autoregressive econometric model to evaluate to what extent and in which direction the rate of participation of potential beneficiaries of the Rural Development Programmes (RDPs) of 2014–2020 is affected by demographic change and other explanatory variables. Regression models compare the results of the OLS (aspatial) and spatial autoregressive models (SAR) of four types of participation rates (all RDP schemes; all LEADER schemes; sectoral schemes of RDP and LEADER; non-sectoral schemes of RDPs and LEADER). This comparison makes it possible to understand the differences between centralised and decentralised management and between sectoral and broader rural-targeted schemes. The results of the models appear attractive in interpreting the role of RDP instruments in different regions and local areas. First, the rate of participation is strongly dependent on macro-regional differences. Regarding the demographic factors at the local level, this study highlights that demographic fragility does not necessarily hamper the use of RDP measures. Conversely, the participation rate in RDP policy schemes seems particularly significant in very fragile areas, whereas significance has yet to be proved in other demographic typologies. This result holds particularly true for the policy uptake of non-sectoral schemes. Furthermore, LEADER decentralised interventions fit the fragile areas more than resilient and vital ones due to the territorially targeted approach followed by the Local Action Groups. Full article

Reducing inequality and fostering economic growth is the tenth global goal of the United Nations for sustainable development. Rail transport significantly influences spatial structures, industrial distributions, and is vital for regional economic integration. Despite its importance, the impact of rail transport on economic equity has not been thoroughly explored in current literature. This study aims to fill this gap by evaluating the spatiotemporal characteristics of rail transport accessibility and its implications for economic equity in the Guangdong–Hong Kong–Macao Greater Bay Area. We considered high-speed, intercity, and conventional rail transport and employ three distinct indicators—door-to-door travel time, weighted average travel time, and potential accessibility—to provide a nuanced assessment of accessibility in the region. Each indicator provides a unique perspective on how accessibility affects economic equity, collectively broadening the scope of the analysis. From 1998 to 2020, the evolution of rail transport and its consequent impact on regional economic equity is scrutinized. Advanced econometric methods, namely ordinary least squares, and spatial Durbin models, are combined with the Gini coefficient and Lorenz curve for comprehensive quantitative analysis. This approach highlights the dynamic influence of rail transport development on economic equity, contributing to the sustainable urban development discourse. The results reveal that although rail transport advancements bolster connectivity and economic growth, they also exacerbate regional economic inequality. This study provides valuable insights for urban planning and policymaking by elucidating the complex relationship between rail transport accessibility and economic equity. Our findings underscore the importance of implementing balanced and inclusive transport policies that foster growth and efficiency while mitigating socioeconomic disparities. Full article

The Sustainable Development Goals are significantly increasing investments in the production of energy from renewable sources (RESs). To this end, the supply of monetary incentives by public institutions has increased sharply. This flow of money inevitably attracts the attention of criminal organizations (henceforth COs) that use their power to increase the volumes of investments, while public authorities might react by deciding not to make investments in RESs in areas at risk of distorted use of incentives. In this context, the research question is as follows: does the presence of COs slow down or encourage investment in RESs? Until now, this topic has received little attention from researchers, at least in the European Union. In particular, the presence of COs is particularly pervasive in the economic system of Italy. Given the heterogeneity of this country, a spatial econometric approach was used, taking into account geographical dependency relationships and their impact on the relevant variables. The main result of the research shows a negative relationship between Italian areas with higher CO levels and RES investments. In other words, investments are discouraged in these regions. This situation is detrimental to the target regions in terms of sustainable development and increasing the gross national product (GNP). Furthermore, we found that micro-crime cannot in any way influence investments in RESs. Full article

This scoping review synthesizes the evidence from eleven key studies to assess the impact of European Structural and Investment Funds (ESIFs) on regional development across the European Union (EU), focusing on fund efficiency, regional disparities and convergence, governance quality, economic freedom, and fund management. A systematic search was conducted across multiple databases to identify the relevant literature published up to 2023. Eleven studies were selected based on the date published and their focus on ESIFs’ role in regional development, employing a range of methodological approaches including Data Envelopment Analysis (DEA), spatial econometrics, and multivariate analyses. The thematic analysis identified four main categories: Methodological Approaches in Evaluating Fund Efficiency, Regional Disparities and Convergence, The Interconnection between Governance Quality, Economic Freedom, and the Efficiency of Structural Fund Management, and The Absorption Capacity and Fund Management. The review highlights the importance of sophisticated analytical tools in evaluating fund efficiency, with DEA and spatial econometrics providing critical insights into fund management efficiency. Studies underscored the nuanced efficacy of ESIFs in reducing regional disparities, albeit pointing to the need for more targeted fund allocation. Governance quality and economic freedom emerged as pivotal factors enhancing fund management efficiency, suggesting the potential of governance reforms in optimizing ESIF allocation and utilization. Challenges related to fund absorption and management were illuminated, advocating for enhanced institutional management capabilities and the development of innovative performance indicators. The findings of this scoping review contribute to a deeper understanding of the complexities surrounding ESIFs’ impact on regional development within the EU. They underscore the critical importance of governance quality, economic freedom, methodological rigor, and strategic fund allocation in enhancing the effectiveness of ESIFs. The review calls for tailored policy interventions and the integration of national and European funding strategies to maximize the impact of these programs on regional development and SME support. Future research should continue to refine these methodological approaches and explore the causal effects of funding, to enhance our understanding of ESIFs’ efficiency in promoting regional development and convergence within the European Union. Full article

Regional green energy technological innovation is an important means to alleviate economic–environmental contradictions. The purpose of this study was to explore the mechanisms of green finance, financial technology, and regional green energy technological innovation. In this study, we constructed dual machine learning models, spatial econometric models, and panel threshold effect models to investigate the effects of green finance and financial technology on regional green energy technological innovation, using panel data from 266 cities nationwide from 2009 to 2021. The research findings are as follows: (1) Both green finance and financial technology significantly promote regional green energy technological innovation. (2) Based on a spatial weight matrix embedded in economic geography, both green finance and financial technology generate positive spatial spillover effects on regional green energy technological innovation. (3) The interaction between green finance and financial technology significantly contributes to regional green energy technological innovation. Financial technology can strengthen the positive local and neighboring effects of green finance on regional green energy technological innovation. (4) Based on the threshold effect of financial technology, green finance cannot significantly promote regional green energy technological innovation when financial technology is in an underdeveloped stage. With the advancement of financial technology, green finance continues to have a positive impact on regional green energy technological innovation. Based on this analysis and our conclusions, we propose practical policy recommendations that can provide a more sustainable approach to green energy technology innovation. Full article

This research investigates the crucial role of transportation infrastructure in influencing economic activity, thus employing advanced econometric methods including Moran’s I index, LM, Hausman, and LR tests to ensure analytical accuracy and select the appropriate spatial model. Our findings reveal that freight volumes across road, waterway, and civil aviation significantly enhance economic activity by bolstering domestic trade, industrial production, and supply chains. Conversely, the impact of passenger turnover is comparatively minor, although it still contributes to labor mobility and urban accessibility. This study highlights the need for strategic investment in transportation infrastructure and efficient public transport systems to foster economic growth and sustainable development. We recommend that policymakers focus on optimizing transportation networks and integrating intelligent transport technologies to boost economic competitiveness and societal well-being. This analysis not only sheds light on the direct economic impacts of transportation but also underscores the broader social implications, thus advocating for a holistic approach to transportation planning and policymaking. Full article

The Solow residual method, traditionally pivotal for calculating total factor productivity (TFP), is typically not applied to green TFP calculations due to its exclusion of undesired outputs. Diverging from traditional approaches and other frontier methodologies such as Data Envelopment Analysis (DEA) and Stochastic Frontier Analysis (SFA), this paper integrates undesired outputs and three types of spatial spillover effects into the conventional Solow framework, thereby creating a new spatiotemporal econometric Solow residual method (STE-SRM). Utilizing this novel method, the study computes the industrial green TFPs for 280 Chinese cities from 2003 to 2019, recalculates these TFPs using DEA-SBM and Bayesian SFA for the same cities and periods, and assesses the accuracy of the STE-SRM-derived TFPs through comparative analysis. Additionally, the paper explores the statistical properties of China’s urban industrial green TFPs as derived from the STE-SRM, employing Dagum’s Gini coefficient and spatial convergence analyses. The findings first indicate that by incorporating undesired outputs and spatial spillover into the Solow residual method, green TFPs are computable in alignment with the traditional Solow logic, although the allocation of per capita inputs and undesired outputs hinges on selecting the optimal empirical production function. Second, China’s urban industrial green TFPs, calculated using the STE-SRM with the spatial Durbin model with mixed effects as the optimal model, show that cities like Huangshan, Fangchenggang, and Sanya have notably higher TFPs, whereas Jincheng, Datong, and Taiyuan display lower TFPs. Third, comparisons of China’s urban industrial green TFP calculations reveal that those derived from the STE-SRM demonstrate broader but more concentrated results, while Bayesian SFA results are narrower and less concentrated, and DEA-SBM findings sit between these extremes. Fourth, the study highlights significant spatial heterogeneity in China’s urban industrial green TFPs across different regions—eastern, central, western, and northeast China—with evident sigma convergence across the urban landscape, though absolute beta convergence is significant only in a limited subset of cities and time periods. Full article

Spatial regression models are widely available across several disciplines, such as functional magnetic resonance imaging analysis, econometrics, and house price analysis. In nature, sparsity occurs when a limited number of factors strongly impact overall variation. Sparse covariance structures are common in spatial regression models. The spatial error model is a significant spatial regression model that focuses on the geographical dependence present in the error terms rather than the response variable. This study proposes an effective approach using the pretest and shrinkage ridge estimators for estimating the vector of regression coefficients in the spatial error mode, considering insignificant coefficients and multicollinearity among regressors. The study compares the performance of the proposed estimators with the maximum likelihood estimator and assesses their efficacy using real-world data and bootstrapping techniques for comparison purposes. Full article

Combining an indicator system developed based on existence–relatedness–growth (ERG) needs and multiple weighting approaches, this paper evaluates the level of high-quality economic development (HQED) in Chinese provinces from the perspective of human well-being from 2007 to 2020. Spatial analysis, Dagum’s Gini coefficient (DGC), and spatial econometric modeling were employed to investigate the spatial–temporal evolutionary characteristics, regional differentiation, and driving factors of HQED in China. The following conclusions are drawn: (1) During the period of 2007–2019, the level of Chinese HQED showed a stable upward trend, and gradually produced the development characteristics of “only super power and multi-great power” and spatial features of “point, line and plane”, with Beijing as the absolute leader, the southeastern coastal region as the advantageous belt, and the relatively advantageous plane in central and western areas with Shaanxi as the core. (2) The degree of spatial differentiation in Chinese provincial HQED narrowed year by year, with intra-regional differentiation organized as follows: eastern > northeastern > western > central; inter-regional differentiation was concentrated in the development gaps across the other three major regions and the eastern areas. (3) Chinese provincial HQED had a significant spatial autocorrelation characteristic, which was further revealed by the spatial Durbin model (SDM) to be a siphon effect at the national and regional levels, i.e., the plundering of the resources and development opportunities of weaker provinces by stronger ones. (4) Driving factors such as economic scale, urbanization level, resource endowment, government size, green technological innovation, industrial structure upgrading, and environmental regulations affected HQED at the national level and in the four major regions to varying degrees. These findings could contribute to policymakers’ efforts to design targeted regional development policies during the transition period of China’s economic development. Full article

The article concerns the recognition of the impact of the COVID-19 pandemic, transport accessibility, and accommodation availability on the energy intensity of domestic travel by tourists using public transport in spatial and dynamic relations. The article formulated five research questions: (1) Does the improvement of transport accessibility reduce the energy intensity of public tourist transport? (2) Does the improvement of accommodation availability affect the reduction of the energy intensity of domestic tourist trips of Polish residents? (3) Has COVID-19 significantly changed the energy intensity of public tourist transport? (4) Are there any spatial effects of energy intensity of domestic tourist trips of Polish inhabitants resulting from the flow of tourists between regions (voivodeships) of Poland? (5) What would be the path of energy intensity patterns of public tourist transport if fortuitous events did not occur? The study covered 16 Polish voivodeships in 2017–2021. A comprehensive approach was used, combining exploratory analysis of spatial data with regional econometrics, spatial statistics, and spatial econometrics (gravitational model of spatial convergence of energy intensity of public transport of tourists). It has been verified that the energy intensity of domestic tourist travel by public transport is the most sensitive to the effects of the COVID-19 pandemic and the most flexible to changes in transport accessibility. It is less sensitive to changes in accommodation availability. The occurrence of spatial convergence, i.e., the blurring of differences in energy intensity patterns between the analyzed voivodeships, was also identified. An increase in energy intensity in voivodeships defined as neighboring voivodeships by 1% will result in an increase in energy intensity in the i-th voivodeship by 0.2688% on average, which results from the spatial effects of changes in mobility and tourist flows (tourism). Consumption patterns shaped in previous periods also have a significant impact on energy intensity. Full article