Regional Sustainability最新文献

Focusing on negative-spillover from environmental effort, this paper explores the issues of competition and optimal decision-making based on two competing two-stage manufacturer-dominated supply chains: centralized and decentralized. We developed the Stackelberg competition models according to four identified competition scenarios (pure centralized structure-CC, mixed structure-DC, mixed structure-CD, and pure decentralized structure-DD). By comparing the results of the four scenarios, we find that negative-spillover from environmental effort negatively impacts environmental-effort providers and reduces the optimal profit of the manufacturer providing the environmental effort, which in turn reduces the profit of the entire supply chain. The inter-supply-chain competition also produces a negative incentive for environmental-effort providers but provides a free riding effect on the non-provider, and this effect increases as competition increases. In terms of network externality, the structural change of a supply chain from centralized to decentralized is altruistic, which generates a double-marginalization (i.e., the network externality enhances competitor performance). Furthermore, the leader supply chain with a centralized structure is more willing to provide greater environmental effort, whereas when the leader supply chain structure remains unchanged, its environmental effort depends on the negative-spillover effect and the follower supply chain structure. Therefore, excessive supply chain competition should be avoided, and the negative-spillover effect of environmental effort should be reduced to motivate environmental-effort providers to increase their environmental efforts and promote the development of sustainable green supply chains. Future research should examine retailer-dominated supply chain competition and compare the results with the conclusions of this study.

Agroecological practices are promoted as a more proactive approach than conventional agriculture to achieving a collective global response to climate change and variability while building robust and resilient agricultural systems to meet food needs and protect the integrity of ecosystems. There is relatively limited evidence on the key traditional agroecological knowledge and practices adopted by smallholder farmers, the factors that influence smallholder farmers’ decision to adopt these practices, and the opportunities it presents for building resilient agricultural systems. Using a multi-scale mixed method approach, we conducted key informant interviews (n ​= ​12), focus group discussions (n ​= ​5), and questionnaire surveys (N ​= ​220) to explore the traditional agroecological knowledge and practices, the influencing factors, and the opportunities smallholder farmers presented for achieving resilient agricultural systems. Our findings suggest that smallholder farmers employ a suite of traditional agroecological knowledge and practices to enhance food security, combat climate change, and build resilient agricultural systems. The most important traditional agroecological knowledge and practices in the study area comprise cultivating leguminous crops, mixed crop-livestock systems, and crop rotation, with Relative Importance Index (RII) values of 0.710, 0.708, and 0.695, respectively. It is reported that the choice of these practices by smallholder farmers is influenced by their own farming experience, access to market, access to local resources, information, and expertise, and the perceived risk of climate change. Moreover, the results further show that improving household food security and nutrition, improving soil quality, control of pest and disease infestation, and support from Non-Governmental Organizations (NGOs) and local authorities are opportunities for smallholder farmers in adopting traditional agroecological knowledge and practices for achieving resilient agricultural systems. The findings call into question the need for stakeholders and policy-makers at all levels to develop capacity and increase the awareness of traditional agroecological knowledge and practices as mechanisms to ensure resilient agricultural systems for sustainable food security.

Evaluation of physical and quantitative data of soil erosion is crucial to the sustainable development of the environment. The extreme form of land degradation through different forms of erosion is one of the major problems in the sub-tropical monsoon-dominated region. In India, tackling soil erosion is one of the major geo-environmental issues for its environment. Thus, identifying soil erosion risk zones and taking preventative actions are vital for crop production management. Soil erosion is induced by climate change, topographic conditions, soil texture, agricultural systems, and land management. In this research, the soil erosion risk zones of Ratlam District was determined by employing the Geographic Information System (GIS), Revised Universal Soil Loss Equation (RUSLE), Analytic Hierarchy Process (AHP), and machine learning algorithms (Random Forest and Reduced Error Pruning (REP) tree). RUSLE measured the rainfall eosivity (R), soil erodibility (K), length of slope and steepness (LS), Land cover and management (C), and support practices (P) factors. Kappa statistic was used to configure model reliability and it was found that Random Forest and AHP have higher reliability than other models. About 14.73% (715.94 km²) of the study area has very low risk to soil erosion, with an average soil erosion rate of 0.00–7.00 × 10³ kg/(hm²·a), while about 7.46% (362.52 km²) of the study area has very high risk to soil erosion, with an average soil erosion rate of 30.00 × 10³–48.00 × 10³ kg/(hm²·a). Slope, elevation, stream density, Stream Power Index (SPI), rainfall, and land use and land cover (LULC) all affect soil erosion. The current study could help the government and non-government agencies to employ developmental projects and policies accordingly. However, the outcomes of the present research also could be used to prevent, monitor, and control soil erosion in the study area by employing restoration measures.

This study examined the impact of climate change on the occurrence of malaria, pneumonia, meningitis, and cholera in Lokoja City, Nigeria with the aim of investigating the spatial distribution and prevalence of the four kinds of diseases as well as the coping strategies of people in the area. We collected the rainfall and temperature data of Lokoja City during 2000–2020 from the National Aeronautics and Space Administration (NASA), and the medical records data of the four kinds of diseases from the Kogi State Ministry of Health. We also conducted a semi-structured questionnaire of 250 residents, who have experienced one of these diseases, to investigate their coping strategies with these diseases. The Pearson correlation and multiple regression analysis were used to research the relationship between the climate parameters and cases of diseases. The result showed annual variations in climatic parameters with R² ​= ​0.0557, 0.0009, and 0.4915 for rainfall, maximum and minimum temperatures respectively. A positive and significant relationship were observed between maximum temperature and malaria (r ​= ​0.80), rainfall and malaria (r ​= ​0.54), minimum temperature and meningitis (r ​= ​0.64), as well as rainfall and cholera (r ​= ​0.66) at P ​< ​0.05. For the regression analysis R² ​= ​0.71, 0.50, and 0.52 for malaria, cholera, and meningitis, respectively at P ​< ​0.05. During 2000–2020, cases of malaria were highest in Ward A with 15,422, while 715 cases of pneumonia were highest in Kupa North. In Ward A 3787 cases of cholera were recorded to be the highest, while cases of meningitis were highest in Kupa North with 2383 cases. Investigation revealed that malaria is more common in the wet season while cholera and meningitis cases were highest in the dry season. The study revealed that the most practiced coping strategy is the use of medications by about 90% of the respondents. More studies are recommended in the study area to establish a causal link between climate change and disease occurrences, and intervention from government in form of prevention and control programmes should be vigorously implemented.

Research on environmental behaviour is often overlooked in literature on regime destabilization in energy transitions. This study addresses that gap by focusing on socio-political and demographic factors shaping support for carbon regime destabilization policies in one of the most carbon-intensive regions of Europe. Carbon-intensive industries, especially coal mining and coal-based power generation, are often concentrated in a few carbon-intensive regions. Therefore, decarbonization actions will affect those regions particularly strongly. Correspondingly, carbon-intensive regions often exert significant political influence on the two climate mitigation policies at the national level. Focusing on Poland, we investigate socio-political and demographic factors that correlate with the approval or rejection of the two climate mitigation policies: increasing taxes on fossil fuels such as oil, gas, and coal and using public money to subsidize renewable energy such as wind and solar power in Poland and its carbon-intensive Silesia region. Using logistic regression with individual-level data derived from the 2016 European Social Survey (ESS) and the 2014 Chapel Hill Expert Survey (CHES), we find party-political ideology to be an important predictor at the national level but much less so at the regional level. Specifically, voting for right-wing party is not a divisive factor for individual support of the two climate mitigation policies either nationally or regionally. More interestingly, populism is a strong factor in support of increasing taxes on fossil fuel in the carbon-intensive Silesia region but is less important concerning in support of using public money to subsidize renewable energy in Poland overall. These results show the heterogeneity of right-wing party and populism within the support for the two climate mitigation policies. Socio-demographic factors, especially age, gender, education level, employment status, and employment sector, have even more complex and heterogeneous components in support of the two climate mitigation policies at the national and regional levels. Identifying the complex socio-political and demographic factors of climate mitigation policies across different national versus carbon-intensive regional contexts is an essential step for generating in situ decarbonization strategies.

Within the context of CO₂ emission peaking and carbon neutrality, the study of CO₂ emissions at the provincial level is few. Sichuan Province in China has not only superior clean energy resources endowment but also great potential for the reduction of CO₂ emissions. Therefore, using logarithmic mean Divisia index (LMDI) model to analysis the influence degree of different influencing factors on CO₂ emissions from final energy consumption in Sichuan Province, so as to formulate corresponding emission reduction countermeasures from different paths according to the influencing factors. Based on the data of final energy consumption in Sichuan Province from 2010 to 2019, we calculated CO₂ emission by the indirect emission calculation method. The influencing factors of CO₂ emissions originating from final energy consumption in Sichuan Province were decomposed into population size, economic development, industrial structure, energy consumption intensity, and energy consumption structure by the Kaya–logarithmic mean Divisia index (LMDI) decomposition model. At the same time, grey correlation analysis was used to identify the correlation between CO₂ emissions originating from final energy consumption and the influencing factors in Sichuan Province. The results showed that population size, economic development and energy consumption structure have positive contributions to CO₂ emissions from final energy consumption in Sichuan Province, and economic development has a significant contribution to CO₂ emissions from final energy consumption, with a contribution rate of 519.11%. The industrial structure and energy consumption intensity have negative contributions to CO₂ emissions in Sichuan Province, and both of them have significant contributions, among which the contribution rate of energy consumption structure was 325.96%. From the perspective of industrial structure, secondary industry makes significant contributions and will maintain a restraining effect; from the perspective of energy consumption structure, industry sector has a significant contribution. The results of this paper are conducive to the implementation of carbon emission reduction policies in Sichuan Province.

Water use and socio-economic development are interconnected in complex ways. Causalities are not easy to identify but it is evident that a nexus between water use and socio-economic development does exist. Considering the diversity of national situations relating to these interrelated phenomena, its study should be considered from a global perspective. This article critically reviews the literature and information from official sources on the relevance of water use and circular economy in order to create a global picture, linking water with socio-economic development. Data from 195 countries was analyzed statistically. A factor analysis defined five essential latent dimensions on the nexus between water use and socio-economic development: development and basic services, population and resource, economic volume, health and well-being, and population density. Based on the identified factors, countries were classified into six groups: Global South in difficulty, global semi-periphery, advanced economy, Middle East and other Global South developing economy, global weight, and small highly developed economy. The clustering results clarify connections between water use conditions and socio-economic development. Understanding the variety of national profiles is helpful to reveal the magnitude and urgency of dealing with the nexus between water use and socio-economic development for many countries.

In recent years, the number of hand, foot, and mouth disease (HFMD) cases in China has continued to grow, and the disease has become a serious public health issue. Xinjiang Uygur Autonomous Region is located in the arid areas of Northwest China, where the epidemiological trend of HFMD is gradually increasing and characterized by geographical heterogeneity. In this study, based on the HFMD case data in all counties and cities in Xinjiang, we employed statistical and GIS spatial analyses, and geographic probe models to characterize the spatiotemporal differentiation of HFMD epidemics in Xinjiang during the period of 2009–2018, and quantitatively analyzed the factors influencing the spatial differentiation of HFMD epidemics. The results showed that HFMD incidence rate in Xinjiang had non-stationary temporal characteristics on the interannual and monthly scales, and the monthly variation characteristics of HFMD epidemic were quite different in southern and northern Xinjiang. The spatial distribution characteristics of HFMD epidemics showed a north–south spatial differentiation pattern with the Tianshan Mountains as the boundary; cold spot and hot spot of HFMD epidemics in Xinjiang have shifted from scattered to concentrated, and the spatial differentiation pattern had gradually stabilized. Moreover, the dominant factors influencing the spatial differentiation of HFMD epidemics in Xinjiang were socioeconomic factors, such as per capita GDP and urbanization rate, while the basic factors affecting its spatial differentiation were natural environmental factors. The spatial differentiation and evolution patterns of HFMD epidemics differed between northern and southern Xinjiang. Specifically, the leading role of socioeconomic factors is more obvious in southern Xinjiang than in northern Xinjiang, while natural environmental factors (e.g., dryness and relative humidity) contribute to the prevalence of HFMD epidemics in northern Xinjiang, and the perturbing effect of these factors was more prominent than other factors. The results of this study can provide a scientific basis for the prevention of HFMD epidemics and early warning of HFMD epidemics in Xinjiang.

Some big cities in Bangladesh have been experiencing a massive and rapid influx of rural people due to the impacts of climate change, and therefore the urban administration encounters enormous challenges. This study aims to investigate the drivers of climate-induced migration and the post-displacement status of the migrants living in the urban slum of Rajshahi City. Using a semi-structured questionnaire survey, this study conducted interviews with 50 migrants residing in two slums in Rajshahi City. An interpretive phenomenological analysis (IPA) approach was implemented to evaluate the survey data. This study finds that food insecurity and flood are the two significant climate drivers of migration. Among the non-climate drivers, lack of alternative livelihood is the major reason. It should be noticed that the climate migrants in many cases do not get the opportunity to improve their living standards; they are usually occupied with low-pay professions like maid, van and rickshaw puller, and scrap collector. The study also reveals that migrants, especially females and children, need several basic physiological, economic, social, and health services. Most children have no chance to attend school. Compared to males, females have more opportunities for some support and allowances. Overall, an inadequate level of change has taken place in the lives of migrants, which raises the concern if migration is ever a way to resolve a problem or the beginning of many other problems. Further researches may concentrate on the impact of migration on the dynamics of social capital among slum dwellers.

Agriculture is not only influenced by climate change, but it is also one of the significant contributors of CO₂ emission. Understanding CO₂ emission and macroeconomic variables is critical to solving the challenges and threats faced by Kenya's agriculture and environment. This study used the Environmental Kuznets Curve (EKC) hypothesis and the autoregressive distribution lag model (ADLM) to analyze the relationships of CO₂ emission with agricultural output, government direct investment, trade openness, and inflation rate in Kenya from 1983 to 2019. The study found that there exists a positive (direct) relationship between CO₂ emission and foreign direct investment in the long run in Kenya. Additionally, CO₂ emission and trade openness have a negative (indirect) and statistically significant relationship after the error correction term adjustment in the long run. Moreover, the relationship between CO₂ emission and agricultural output is positive (direct) and statistically significant in the long run. There is a positive (direct) and statistically insignificant relationship between CO₂ emission and inflation rate in the short run. Notably, the EKC hypothesis indicated that the Kenya's economy is still on the environmental degradation trade-off through the gradual increase of both CO₂ emission and agricultural output. Our results are important to Kenya's economy because the derived insights will assist in relevant departments to formulate sustainable strategies to minimize environmental degradation.