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By analyzing the mechanisms by which rural infrastructure resilience (RIR) impacted population loss in Longxi County, this study proposes measures to improve RIR, which provides a practical reference for realizing China's rural revitalization strategy, besides providing ideas for alleviating population loss in similar regions around the world.

This study considered 213 administrative villages in Longxi County in the Longzhong loess hilly region as the evaluation unit. Based on the construction of a multidimensional RIR evaluation system, the spatial spillover effect of RIR on population loss was determined using the spatial Durbin model (SDM).

The average resilience of each subsystem of rural infrastructure in Longxi County was low, and there were large differences in the spatial distribution. The mean RIR index value was 0.2258, with obvious spatial directivity and agglomeration characteristics. The population loss index of Longxi County had a value of 0.1759, with 26.29 of villages having a high loss level. The population loss was relatively serious and was correlated with the spatial distribution of RIR. The villages with larger RIR index values had lower population loss. The RIR had a significant spatial spillover effect on population loss. Productive infrastructure resilience and living infrastructure resilience (LIR) had negative spillover effects on population loss, and social service infrastructure resilience (SSIR) had a positive spillover effect on population loss.

By analyzing the mechanisms by which RIR impacted on population loss in Longxi County, this study proposes measures to improve RIR, which provides a practical reference for realizing China's rural revitalization strategy, besides providing ideas for alleviating population loss in similar regions around the world.

Examine the effects of sudden environmental disasters on the advancement of both renewable and conventional energy technologies.

Utilizing panel data from 31 Chinese provinces spanning 2011 to 2022, the SEM (Spatial Error Model) dual fixed model is utilized to examine the impact of sudden environmental disasters on energy technologies.

The findings reveal that: (1) Sudden environmental disasters exert a markedly positive influence on the Innovation of Renewable Energy Technologies (IRET), while their impact on conventional energy technologies is positively non-significant. (2) Sudden environmental disasters not only significantly enhance innovation in local renewable energy technologies but also extend this positive influence to neighboring regions, demonstrating a spatial spillover phenomenon. (3) Research and Development (R&D) funding serves as a partial mediator in the relationship between sudden environmental disasters and renewable ETI. In contrast, Foreign Direct Investment (FDI) exhibits a masking effect.

Consequently, the study advocates for intensified efforts in post-disaster reconstruction following abrupt environmental events, an elevation in the quality of foreign direct investments, and leveraging research funding to catalyze innovation in renewable energy technologies amid unforeseen environmental crises.

The research objective of this paper is to investigate the direct and indirect impacts of green finance on agricultural carbon total factor productivity (ACTFP) within the framework of the carbon peaking and carbon neutrality (dual carbon) goals, while also identifying the driving factors through an exponential decomposition of ACTFP, aiming to provide policy recommendations to enhance financial support for low-carbon agricultural development.

In this paper, the Global Malmquist Luenberger (GML) Index method was employed to analyze and decompose the ACTFP, while the direct and spillover effects of China’s green finance pilot policy (GFPP) on ACTFP were assessed using the difference-in-differences (DID) method and the spatial differences-in-differences (SDID) method, respectively.

After the implementation of the GFPP, the ACTFP in the pilot area has experienced significant improvement, with the enhancement of technical efficiency serving as the main driving force. In addition, the GFPP exhibits a positive low-carbon spatial spillover effect, indicating it benefits ACTFP in both the pilot and adjacent areas.

Within the framework of the dual carbon goals, the paper highlights agriculture as a significant carbon emitter. ACTFP is assessed by considering the agricultural carbon emission factor as the sole non-desired output, and the impact of the GFPP on ACTFP is investigated through the DID method, thereby providing substantial validation of the hypotheses inferred from the mathematical model. Subsequently, the spillover effects of GFPP on ACTFP are analyzed in conjunction with the spatial econometric model.

This study aims to investigate the spatial distribution of housing prices and identify the affecting factors (independent variable) on the cost of residential units (dependent variable).

The method of the present study is descriptive-analytical and has an applied purpose. The used statistical population in this study is the residential units’ price in Tehran in 2021. For this purpose, the average per square meter of residential units in the city neighborhoods was entered in the geographical information system. Two techniques of ordinary least squares regression and geographically weighted regression have been used to analyze housing prices and modeling. Then, the results of the ordinary least squares regression and geographically weighted regression models were compared by using the housing price interpolation map predicted in each model and the accurate housing price interpolation map.

Based on the results, the ordinary least squares regression model has poorly modeled housing prices in the study area. The results of the geographically weighted regression model show that the variables (access rate to sports fields, distance from gas station and water station) have a direct and significant effect. Still, the variable (distance from fault) has a non-significant impact on increasing housing prices at a city level. In addition, to identify the affecting variables of housing prices, the results confirm the desirability of the geographically weighted regression technique in terms of accuracy compared to the ordinary least squares regression technique in explaining housing prices. The results of this study indicate that the housing prices in Tehran are affected by the access level to urban services and facilities.

Identifying factors affecting housing prices helps create sustainable housing in Tehran. Building sustainable housing represents spending less energy during the construction process together with the utilization phase, which ultimately provides housing at an acceptable price for all income deciles. In housing construction, the more you consider the sustainable housing principles, the more sustainable housing you provide and you take a step toward sustainable development. Therefore, sustainable housing is an important planning factor for local authorities and developers. As a result, it is necessary to institutionalize an integrated vision based on the concepts of sustainable development in the field of housing in the Tehran metropolis.

This study aims to clarify the impact of agriculture–tourism integration (ATI) on in situ urbanization (ISURB) of rural residents, to highlight the role of industrial integration in the process of China's ISURB and to provide industrial integration suggestions for promoting urbanization quality in Chinese counties.

By sorting out the panel data of China's 1868 counties, the evaluation index system of ISURB was constructed. Difference in difference (DID) and spatial Durbin-difference in difference (SDM-DID) model is used for estimate the relationship between ATI and ISURB.

First, ATI can improve ISURB by 11.4% higher than other regions. Second, theoretical analysis model of ATI on ISURB is constructed from four aspects of “drive–push–pull–block.” The results show that ATI can promote ISURB by increasing upgrading of rural industries, rural employment demand and income capacity, whereas ATI may inhibit ISURB by reducing farmland. Third, considering changes in institutional, hard and soft factors, rural collective economy, information infrastructure and digital finance all promote positive impact of ATI on ISURB. Fourth, ATI will produce spillover effects on ISURB in neighboring regions, which is more pronounced in the central and western regions.

This study lacks quantification of ATI, so future studies are encouraged to further quantify ATI at the county level.

This study has policy significance for constructing ATI demonstration counties and promoting ISURB in China's counties.

It is of great practical value to promote China's ISURB. By stimulating ATI, it can improve income and employment capacity of rural residents and stimulate ISURB of China.

This study enriches the theoretical and practical research on industrial integration behaviors during the process of ISURB.

1. Use county data to measure in situ urbanization (ISURB)

2. Agriculture–tourism integration (ATI) can increase ISURB

3. Constructs a “drive-push-pull-block” model to explain the influence mechanism

4. Use spatial Durbin-difference in difference (SDM-DID) models

5. Consider collective economy, rural information infrastructure and digital finance

Use county data to measure in situ urbanization (ISURB)

Agriculture–tourism integration (ATI) can increase ISURB

Constructs a “drive-push-pull-block” model to explain the influence mechanism

Use spatial Durbin-difference in difference (SDM-DID) models

Consider collective economy, rural information infrastructure and digital finance

Land value varies at a micro level depending on the location’s economic, geographical and political determinants. The purpose of this study is to present a comprehensive assessment of the determinants affecting land value in the Indian city of Thiruvananthapuram in the state of Kerala.

The global influence of the identified 20 explanatory variables on land value is measured using the traditional hedonic price modeling approach. The localized spatial variations of the influencing parameters are examined using the non-parametric regression method, geographically weighted regression. This study used advertised land value prices collected from Web sources and screened through field surveys.

Global regression results indicate that access to transportation facilities, commercial establishments, crime sources, wetland classification and disaster history has the strongest influence on land value in the study area. Local regression results demonstrate that the factors influencing land value are not stationary in the study area. Most variables have a different influence in Kazhakootam and the residential areas than in the central business district region.

This study confirms findings from previous studies and provides additional evidence in the spatial dynamics of land value creation. It is to be noted that advanced modeling approaches used in the research have not received much attention in Indian property valuation studies. The outcomes of this study have important implications for the property value fixation of urban Kerala. The regional variation of land value within an urban agglomeration shows the need for a localized method for land value calculation.

The purpose of this study is to analyze the effect of foreign direct investment (FDI) on pollution emissions and how environmental regulation affects this relationship.

In the empirical research, the authors selected panel data for 30 provinces in China from 2005 to 2019 as samples. First, the authors used the instrumental variable method to verify the existence of the above hypotheses in China. Then, the authors analyzed the moderating effect of different types of environmental regulations on the environmental effects of FDI. Next, in further discussion, the authors analyzed the difference between the environmental effect and the moderating effect in different time periods and regions, respectively. Finally, the authors discussed whether the different intensities of environmental regulations lead to the transfer effect of FDI in choosing investment destinations.

The result shows that FDI can help reduce pollution emissions and create a “pollution halo” effect, which is enhanced by command-and-control regulation but suppressed by market-based incentives. The heterogeneity analysis reveals that the 18th National Congress of the Communist Party has weakened the pollution halo effect of FDI, while the environmental effect of FDI in the eastern region is not significant, but in the middle and western regions, there is a significant pollution halo effect and a positive moderating effect of environmental regulations. Finally, further analysis reveals that FDI has a transfer effect under command-and-control environmental regulations.

First, the main purpose of this paper is to study the relationship between FDI and pollution emissions from the perspective of heterogeneous environmental regulation. Therefore, there is no detailed discussion on their effect mechanism of them. Second, limited by data, the authors adopt the single index to measure the stringency index of command-and-control and market-based incentive environmental regulations in China. The single index may not be able to fully reflect the intensity of regional environmental regulation, so the construction of a composite indicator is necessary. These shortcomings are the focus of the authors' future research.

Under the guidance of high-quality development, the conclusions above can provide reference for adjusting FDI policies and improving environmental regulation policies.

The innovations in this paper can be summarized as the following four dimensions: First, the authors use the instrumental variable (IV) method to address endogeneity in the relationship between FDI and pollution emission, which can further ensure the robustness of the research results and increases the credibility of the paper. Second, the authors distinguish between two types of environmental regulations to investigate their moderating effect on the environmental impact of FDI. Third, the authors consider the temporal and spatial heterogeneity of both the environmental effects of FDI and the moderating effect of regulation. Last, the authors analyze the spatial spillover of environmental regulation through the study of the transfer effect.

The aim of this paper is to analyse the spillover effects of microcredit on the economy of Ecuador, with a particular focus on its potential as a poverty alleviation mechanism.

To address our research questions, we take into account the distance between cantons (Ecuador’s own administrative distribution) by adopting a spatial autoregressive (SAR) model. To this end, a database will be constructed with macroeconomic information about the country, broken down by canton (administrative division of Ecuador), and in a 2019 cross section, with a total of 1,331 microcredit operations in all 221 of Ecuador’s cantons.

We find a positive effect of microcredit on these neighbouring regions in terms of wealth generation.

We acknowledge that this study is limited to Ecuadorian cantons. Nonetheless, it is crucial to emphasize that focussing on an under-represented developing country like Ecuador adds significant value to the research.

Facilitating access to microcredit is one of the main solutions to address the goals proposed in the sustainable development goals (SDGs).

Microcredit activity contributes to the creation of value and wealth in Ecuador, exerting a spillover effect in neighbouring areas that can generate positive multiplier effects and alleviate poverty. For all of the above reasons, our proposal for the country is to support and promote microcredit as one of the main solutions to address the goals proposed in the SDGs.

The novelty of this study lies in the use of spatial econometrics to observe the indirect effects of microcredit on the regions bordering the canton in which it was issued, thus examining the spatial effects of microcredit on wealth distribution.

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

The present study investigates the effect of foreign direct investment (FDI) and governance quality on carbon emissions in the Economics Community of West African States (ECOWAS).

To achieve the objective of this research, panel data for dependent and explanatory variables over the period 2005–2016, collected in the World Development Indicators (WDI) database and World Governance Indicators (WGI), are analyzed using the generalized method of moments (GMM). Also, the panel-corrected standard errors (PCSE) method is applied to the four segments of the overall sample to analyze the stability of the results.

The findings of this study are: (1) FDI inflows have a negative effect on carbon emissions in ECOWAS and (2) The interaction between FDI inflows and governance quality have a negative effect on carbon emissions. These results show the decreasing of environmental damage by increasing institutional quality. However, the estimation results on the country subsamples show similar and non-similar aspects.

This study suggests that policymakers in the ECOWAS countries should strengthen their environmental policies while encouraging FDI flows to be environmentally friendly.

The subject has rarely been explored in West Africa, with gaps such as the lack of use of institutional variables. This study contributes to the literature by drawing on previous work to examine the role of good governance on FDI and the CO2 emission relationship in the ECOWAS, which have received little attention. However, this research differs from previous work by subdividing the overall sample into four groups to test the stability of the results.