Artisanal gold mining, a labor-intensive and antiquated technique, is a growing industry and the source of income for rural communities all over the world. However, artisanal gold mining has potential negative and long-term effects on economy, environment, and society. This study collected soil samples from 16 sample points including a control point to examine the pollution degrees and spatial distribution of heavy metals, as well as ecological and health risks associated with heavy metal pollution in the Ijero-Ekiti mining site, Nigeria. Geographical Information System (GIS) and remote sensing technologies were used to identify regions with high concentrations of heavy metals and assess the environmental impact of gold mining activities. The results show that the mean heavy metal concentrations of 16 soil pointa are 8.94 (±5.97) mg/kg for As, 0.18 (±0.54) mg/kg for Cd, 0.11 (±1.06) mg/kg for Co, 14.32 (±3.43) mg/kg for Cr, 6.89 (±0.64) mg/kg for Cu, 48.92 (±11.77) mg/kg for Fe, 135.81 (±30.75) mg/kg for Mn, 5.92 (±0.96) mg/kg for Ni, 5.72 (±1.66) mg/kg for Pb, and 13.94 (±1.38) mg/kg for Zn. The study reveals that heavy metal concentration in soils follows the order of Mn>Fe>Cr>Zn>As>Cu>Ni>Pb>Co>Cd. An analysis of soil samples indicates that 3 principal components (PCs) account for 70.008% of the total variance and there are strong positive correlations between various pairs of heavy metals. The total potential ecological risk index (309.599) in the study area is high. Non-carcinogenic risk suggests that there may be long-term health impacts on people who work in the mining areas due to chronic exposure to the environment. Based on the study, the hazard index of carcinogenic health risks associated with heavy metals through ingestion is 520.00×10^-4. Dermal contact from As and Cr also increases the risk of cancer, with the highest hazard index value of 18.40×10^-4. The lowest exposure pathway, with the hazard index value of 0.68×10^-4, indicates that the inhalation of heavy metals has a comparatively low risk of cancer. This study recommends the formulation of policies to monitor the Ijero-Ekiti mining site and other regions in Nigeria where indiscriminate artisanal gold mining activities exist.

“Human-elephant conflict (HEC)”, the alarming issue, in present day context has attracted the attention of environmentalists and policy makers. The rising conflict between human beings and wild elephants is common in Buxa Tiger Reserve (BTR) and its adjoining area in West Bengal State, India, making the area volatile. People’s attitudes towards elephant conservation activity are very crucial to get rid of HEC, because people’s proximity with wild elephants’ habitat can trigger the occurrence of HEC. The aim of this study is to conduct an in-depth investigation about the association of people’s attitudes towards HEC with their locational, demographic, and socio-economic characteristics in BTR and its adjoining area by using Pearson’s bivariate chi-square test and binary logistic regression analysis. BTR is one of the constituent parts of Eastern Doors Elephant Reserve (EDER). We interviewed 500 respondents to understand their perceptions to HEC and investigated their locational, demographic, and socio-economic characteristics including location of village, gender, age, ethnicity, religion, caste, poverty level, education level, primary occupation, secondary occupation, household type, and source of firewood. The results indicate that respondents who are living in enclave forest villages (EFVs), peripheral forest villages (PFVs), corridor village (CVs), or forest and corridor villages (FCVs), mainly males, at the age of 18-48 years old, engaged with agriculture occupation, and living in kancha and mixed houses, have more likelihood to witness HEC. Besides, respondents who are illiterate or at primary education level are more likely to regard elephant as a main problematic animal around their villages and refuse to participate in elephant conservation activity. For the sake of a sustainable environment for both human beings and wildlife, people’s attitudes towards elephants must be friendly in a more prudent way, so that the two communities can live in harmony.

The county (city) located on the northern slope of the Kunlun Mountains is the primary area to solidify and extend the success of Xinjiang Uygur Autonomous Region, China in poverty alleviation. Its Sustainable Development Goals (SDGs) are intertwined with the concerted economic and social development of Xinjiang and the objective of achieving shared prosperity within the region. This study established a sustainable development evaluation framework by selecting 15 SDGs and 20 secondary indicators from the United Nations’ SDGs. The aim of this study is to quantitatively assess the progress of SDGs at the county (city) level on the northern slope of the Kunlun Mountains. The results indicate that there are substantial variations in the scores of SDGs among the nine counties and one city located on the northern slope of the Kunlun Mountains. Notable high scores of SDGs are observed in the central and eastern regions, whereas lower scores are prevalent in the western areas. The scores of SDGs, in descending order, are as follows: 62.22 for Minfeng County, 54.22 for Hotan City, 50.21 for Qiemo County, 42.54 for Moyu County, 41.56 for Ruoqiang County, 41.39 for Qira County, 39.86 for Lop County, 38.25 for Yutian County, 38.10 for Pishan County, and 36.87 for Hotan County. The performances of SDGs reveal that Hotan City, Lop County, Minfeng County, and Ruoqiang County have significant sustainable development capacity because they have three or more SDGs ranked as green color. However, Hotan County, Moyu County, Qira County, and Yutian County show the poorest performance, as they lack SDGs with green color. It is important to establish and enhance mechanisms that can ensure sustained income growth among poverty alleviation beneficiaries, sustained improvement in the capacity of rural governance, and the gradual improvement of social security system. These measures will facilitate the effective implementation of SDGs. Finally, this study offers a valuable support for governmental authorities and relevant departments in their decision-making processes. In addition, these results hold significant reference value for assessing SDGs at the county (city) level, particularly in areas characterized by low levels of economic development.

The abandonment of date palm grove of the former Al-Ahsa Oasis in the eastern region of Saudi Arabia has resulted in the conversion of delicate agricultural area into urban area. The current state of the oasis is influenced by both expansion and degradation factors. Therefore, it is important to study the spatiotemporal variation of vegetation cover for the sustainable management of oasis resources. This study used Landsat satellite images in 1987, 2002, and 2021 to monitor the spatiotemporal variation of vegetation cover in the Al-Ahsa Oasis, applied multi-temporal Normalized Difference Vegetation Index (NDVI) data spanning from 1987 to 2021 to assess environmental and spatiotemporal variations that have occurred in the Al-Ahsa Oasis, and investigated the factors influencing these variation. This study reveals that there is a significant improvement in the ecological environment of the oasis during 1987-2021, with increase of NDVI values being higher than 0.10. In 2021, the highest NDVI value is generally above 0.70, while the lowest value remains largely unchanged. However, there is a remarkable increase in NDVI values between 0.20 and 0.30. The area of low NDVI values (0.00-0.20) has remained almost stable, but the region with high NDVI values (above 0.70) expands during 1987-2021. Furthermore, this study finds that in 1987-2002, the increase of vegetation cover is most notable in the northern region of the study area, whereas from 2002 to 2021, the increase of vegetation cover is mainly concentrated in the northern and southern regions of the study area. From 1987 to 2021, NDVI values exhibit the most pronounced variation, with a significant increase in the “green” zone (characterized by NDVI values exceeding 0.40), indicating a substantial enhancement in the ecological environment of the oasis. The NDVI classification is validated through 50 ground validation points in the study area, demonstrating a mean accuracy of 92.00% in the detection of vegetation cover. In general, both the user’s and producer’s accuracies of NDVI classification are extremely high in 1987, 2002, and 2021. Finally, this study suggests that environmental authorities should strengthen their overall forestry project arrangements to combat sand encroachment and enhance the ecological environment of the Al-Ahsa Oasis.

Growing climate change concerns have intensified the focus on agribusiness sustainability, driving an urgent energy transition to improve production efficiency and mitigate environmental harm. The complex interplay between energy efficiency and energy consumption highlights the essential role of strategic energy policies in ensuring sustainable development. This study used the Double-Log regression model with bootstrap resampling to examine the rebound effect in the energy transition of agribusiness focusing on five Latin American countries including Brazil, Argentina, Uruguay, Colombia, and Mexico based on the agricultural sector data during 2010-2022. The findings revealed that the rebound effect significantly influences energy transition, with varying degrees of impact across agricultural sectors. This study identified partial rebound effect across all five countries, with elasticity coefficient varying from 9.63% (Colombia’s coffee sector) to 89.12% (Brazil’s livestock sector). In Brazil’s sugarcane sector, non-renewable energy, agricultural employment, and irrigation efficiency were identified as key factors influencing energy consumption, while in livestock sector, energy consumption was affected by CH₄ emissions, income and well-being of farmers, water consumption, and water conservation practices. In Mexico’s livestock sector, CH₄ emissions, non-renewable energy, and water conservation practices were the key factors affecting energy consumption. In Argentina’s sugarcane sector, pesticides, NO₂ emissions, renewable energy, and agricultural employment were the key factors affecting energy consumption, while renewable energy, income and well-being of farmers, and water consumption were the key factors affecting energy consumption in livestock sector. In Uruguay’s livestock sector, non-renewable energy, income and well-being of farmers, and irrigation efficiency were the key factors affecting energy consumption. In Colombia’ coffee sector, NO₂ emissions and irrigation efficiency were identified as key factors influencing energy consumption. Finally, this study reinforces the importance of aligning energy transition with Sustainable Development Goals (SDGs), ensuring that energy efficiency gains do not inadvertently increase energy consumption or environmental degradation.

As the impact of climate change intensifies, climate migration (climate change-induced migration) has become a pressing global issue that requires effective adaptation strategies to lessen its effects. Therefore, this study delved into the complex relationship between climate change adaptation strategies and climate migration with food insecurity serving as a mediating factor. We collected sample data through face-to-face interviews in Khorramabad City, Iran from February to May in 2023. Using the Structural Equation Modeling (SEM), we explored how food insecurity influences the relationship between climate change adaptation strategies and climate migration. The findings showed that while climate change adaptation strategies can boost community resilience, their success is closely tied to levels of food insecurity. About 78.72% of the surveyed households experienced certain levels of food insecurity, increasing the risk of displacement due to climate-related disasters. Climate change adaptation strategies including economic strategies, irrigation management strategies, organic-oriented strategies, sustainable development-oriented strategies, and crop variety management strategies played a significant role in reducing climate migration. Moreover, we found that climate change adaptation strategies not only impact food security, but also shape migration decisions. This research underscores the importance of an integrated approach that links climate change adaptation strategies, climate migration, and food insecurity. This study emphasizes the importance of food security for formulating sustainable adaptation strategies.

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis-Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991-2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991-2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.

Desertification poses a significant ecological threat to global sustainability, notably within arid regions such as the Tarim Basin surrounding the extensive Taklimakan Desert in Northwest China. This study used the Google Earth Engine (GEE) platform and Random Forest (RF) to analyze multi-temporal Landsat images to reveal desertification dynamics in the Tarim Basin spanning from 1990 to 2020. The results showed that land use types of the Tarim Basin were classified into three types: artificial oases, natural oases, and desertified land. To robustly quantify the spatiotemporal dynamics of land use, we introduced the Desertification Change Index (DCI), a novel metric specifically designed to measure the transformation sensitivity of land use types. Our analysis demonstrated that from 1990 to 2020, artificial oases showed a continuous increasing trend, while desertified land decreased significantly. Natural oases decreased during 1990-2000, then continued to grow, and showed a decreasing trend again after 2015. Moreover, natural oases have mainly been converted into desertified land and artificial oases. Artificial oases were mainly converted into natural oases, and most of desertified land was converted into natural oases. Regions with significant oasis expansion (DCI=2) were mostly concentrated within the influence radius of artificial oases. In contrast, regions with significant oasis degradation (DCI= -2) were generally farther from artificial oases. Finally, this study found that the changes in land use types of the Tarim Basin are mainly driven by human activities, which play a dual role—mitigating desertification by controlling oasis expansion and exacerbating desertification through unsustainable resource utilization. Ultimately, this research provides essential insights for policy-makers and land managers aiming to devise adaptive and sustainable desertification control measures in the Tarim Basin and similarly arid regions globally.

The rapid urbanization and increasing challenges are faced by cities globally, including climate change, population growth, and resource constraints. Sustainable smart city (also referred to as “smart sustainable city”) can offer innovative solutions by integrating advanced technologies to build smarter, greener, and more livable urban environments with significant benefits. Using the Web of Science (WoS) database, this study examined: (i) the mainstream approaches and current research trends in the literature of sustainable smart city; (ii) the extent to which the research of sustainable smart city aligns with Sustainable Development Goals (SDGs); (iii) the current topics and collaboration patterns in sustainable smart city research; and (iv) the potential opportunities for future research on the sustainable smart city field. The findings indicated that research on sustainable smart city began in 2010 and gained significant momentum in 2013, with China leading, followed by Italy and Spain. Moreover, 59.00% of the selected publications on the research of sustainable smart city focus on SDG 11 (Sustainable Cities and Communities). Bibliometric analysis outcome revealed that artificial intelligence (AI), big data, machine learning, and deep learning are emerging research fields. The terms smart city, smart cities, and sustainability emerged as the top three co-occurring keywords with the highest link strength, followed by frequently co-occurring keywords such as AI, innovation, big data, urban governance, resilience, machine learning, and Internet of Things (IoT). The clustering results indicated that current studies explored the theoretical foundation, challenges, and future prospects of sustainable smart city, with an emphasis on sustainability. To further support urban sustainability and the attainment of SDGs, the future research of sustainable smart city should explore the application and implications of AI and big data on urban development including cybersecurity and governance challenges.

The tourism industry is economically very important. According to the World Travel Tourism Council, in 2019, the tourism industry accounted for a quarter of all new jobs created worldwide, 10.3% of all jobs, and 9.6×10¹² USD of the global gross domestic product. This study aimed to calculate the tourism efficiency index for different Latin American countries from 2010 to 2021 using data envelopment analysis, which analyzes the relationships between input variables (including the number of employees in the tourism industry and the number of hotel-type establishments) and output variables (including tourism expenditures in other countries and public social expenditures in recreation and culture per capita). Additionally, this study aimed to identify the countries with greater tourism development and the factors that may affect the development of the tourism industry through the stochastic frontier production function. The results of the tourism efficiency index for Central America (including Costa Rica, Dominica, El Salvador, Honduras, Mexico, and Panama) and South America (including Argentina, Brazil, Chile, Colombia, Ecuador, Paraguay, Peru, and Uruguay) exhibited different trends. However, after the global health crisis, the tourism industry recovered, showing new opportunities to promote sustainability. The results of the stochastic frontier production function demonstrated that countries with higher levels of inbound and outbound tourism, contribution of tourism to the economy, natural resources, and literacy rate exhibited more efficient tourism industry, whereas countries with higher pollution levels exhibited less efficient tourism industry. The findings of this study could allow us to formulate suitable public policies to promote tourism, maintain natural resources, and diversify these sectors with more inclusive programmes that can facilitate growth and benefit vulnerable communities.

Peri-urban areas are playing an increasingly crucial role in the agricultural development and intensification in Indonesia. Peri-urban agriculture is highly vulnerable to change compared with urban and rural agriculture, due to its location in transitional areas. Indicators of peri-urban agricultural intensity can help guide regional development. In this study, we assessed the sustainability of peri-urban areas based on agricultural intensity in Karawang Regency, Indonesia. We developed a village-based index to assess the region’s agricultural intensity by rescaling the village agriculture index (VAI) and combining the local sustainability index (LSI) with factor analysis. Since the unit of analysis is the village, we modified the LSI to the village sustainability index (VSI). In addition, we also developed a logical matrix analysis to determine the level of agricultural sustainability (LoAS) of each village. The combined results of the three indices (VAI, VSI, and LoAS) generated information about agricultural sustainability. The results indicated that peri-urban villages with high agricultural intensity tended to exhibit low levels of social welfare, economic development, and disaster risk. Moreover, high agricultural intensity did not necessarily ensure the prosperity of the people. Instead, there was the economic disparity among the villages in the study area. Encouraging diversity of agricultural intensity seems to be more critical than promoting agricultural intensity itself. Overall, this study highlights the distinctive characteristics and dynamic of peri-urban areas. New approaches, variables, and information regarding the combination of agricultural intensity and sustainability need to be developed as valuable tools for regional planning.

Road transport plays a crucial role in facilitating mobility and the movement of goods, particularly in the Extended Bangkok Metropolitan Region (EBMR), Thailand. This area is undergoing rapid industrialization and urbanization, resulting in significant energy consumption and greenhouse gas (GHG) emissions. This study examined the relationships among individual socioeconomic factors, travel characteristics, and energy consumption characteristics and their impacts on GHG emissions from road transport. The path analysis technique was applied to identify the key driving factors and their causal relationships. The data were collected through 1600 questionnaire surveys with road drivers in representative areas of the EBMR from December 2022 to May 2023. The results revealed that individual socioeconomic factors significantly influenced GHG emissions from road transport. Among the drivers, factors such as income, age, education, and driving experience indirectly influenced travel characteristics and energy consumption characteristics, impacting GHG emissions. Similarly, individual socioeconomic factors affected the travel characteristics of tourists and personal travelers. Driving experience was a crucial factor for public road transport and freight vehicle drivers, influencing travel characteristics and contributing to GHG emissions. These findings highlight the importance of key policy recommendations, such as promoting the adoption of electric vehicles, optimizing public transport, incentivizing low-emission tourism, and modernizing freight transport with clean technologies, to enhance efficiency, reduce emissions, and support regional sustainability. This study provides policy-makers with insights into the key factors influencing GHG emissions across different driving factors, revealing how individual socioeconomic factors impact travel characteristics and energy consumption characteristics. The findings will inform the development of targeted emission reduction strategies and sustainable transport policies.

The use of renewable energy is an important way to achieve sustainable agricultural and economic development. However, there are differences in access to renewable energy between the Global North and Global South. This study utilised an autoregressive distributed lag-error correction model and the data spanning from 1991 to 2021 to comparatively analyse the dynamic relationship among renewable energy consumption, the value of agricultural production, gross domestic product (GDP), economic diversification index, urban population, the total water extraction for agricultural withdrawal, and trade balance in the Netherlands and South Africa. In the short run, renewable energy consumption was increased by the value of agricultural production but decreased by GDP in South Africa. In the long run, renewable energy consumption and GDP increased the value of agricultural production, while the value of agricultural production also increased GDP in South Africa. However, in the Netherlands, there was no short- and long-run relationship between renewable energy consumption and agricultural and economic development. The results revealed that there was a short- and long-run relationship in South Africa. Moreover, in the Netherlands, the adjustment speed was -1.46 for renewable energy consumption with an error correction of 0.68 a (8.22 months). In South Africa, the adjustment speed was -1.28 for renewable energy consumption with an error correction of 0.78 a (9.38 months). Therefore, compared to South Africa, renewable energy consumption in the Netherlands takes less time to return to balance after a shock. These findings signify different trajectories on sectoral and economic transition initiatives spurred using renewable energy between the Netherlands and South Africa. Policy relating to initiatives such as “agro-energy communities” in Global South countries such as South Africa should be emphasised to promote the use of renewable energy in the agricultural sector.

The economic potential induced by environmental changes in the Arctic region garnered substantial interest, which positions Arctic trade as a crucial indicator in forecasting the impacts of climate change on the global economy. Nevertheless, attention devoted to the evolving dynamics of trade in the Arctic region remains scarce. In this study, we constructed export trade network in the Arctic region (including Denmark, Finland, Sweden, Norway, Iceland, the Canadian Arctic, the Russian Arctic, Alaska State of the USA, and Greenland) from 1990 to 2019 and analyzed its topology and evolutionary characteristics through complex network theory. We used a structural entropy index based on the distribution of the number of trading partners and the degree of trade concentration to assess export diversity, while we also utilized a revealed comparative advantage index to evaluate product export competitiveness using the share of trade volume of each type of product. The results indicate that the total export trade in the Arctic region increased by 53.4% during 1990-2019, with the most significant growth observed in the exports of chemical products and mineral fuels. The increasing complexity of trade network in the Arctic region resulted in the region’s export destinations no longer being concentrated on a few major countries and regions. The proportion of exports from the Arctic region to Europe decreased by 13.5%, while the proportion of exports from the Arctic region to Asia and North America increased by 6.8% and 3.1%, respectively. The Arctic region exhibited clear distinctions in the range of flows of different products, and its export trade was becoming increasingly diversified. Although differences in comparative advantages between products within individual countries or regions have narrowed, substantial gaps persist. The findings of this study can enhance the comprehensive understanding of the significance and function of Arctic trade activities within the global economy, providing a scientific basis for addressing the associated challenges and opportunities in the context of climate change.

Climate change and increasing anthropogenic activities, such as over-exploitation of groundwater, are exerting unavoidable stress on groundwater resources. This study investigated the spatio-temporal variation of depth to groundwater level (DGWL) and the impacts of climatic (precipitation, maximum temperature, and minimum temperature) and anthropogenic (gross district product (GDP), population, and net irrigated area (NIA)) variables on DGWL during 1994-2020. The study considered DGWL in 113 observation wells and piezometers located in arid western plains (Barmer and Jodhpur districts) and semi-arid eastern plains (Jaipur, Ajmer, Dausa, and Tonk districts) of Rajasthan State, India. Statistical methods were employed to examine the annual and seasonal patterns of DGWL, and the generalized additive model (GAM) was used to determine the impacts of climatic and anthropogenic variables on DGWL. During 1994-2020, except for Barmer District, where the mean annual DGWL was almost constant (around 26.50 m), all other districts exhibited increase in DGWL, with Ajmer District experiencing the most increase. The results also revealed that 36 observation wells and piezometers showed a statistically significant annual increasing trend in DGWL and 34 observation wells and piezometers exhibited a statistically significant decreasing trend in DGWL. Similarly, 32 observation wells and piezometers showed an statistically significant increasing trend and 37 observation wells and piezometers showed a statistically significant decreasing trend in winter; 33 observation wells and piezometers indicated a statistically significant increasing trend and 34 had a statistically significant decreasing trend in post-monsoon; 35 observation wells and piezometers exhibited a statistically significant increasing trend and 32 observation wells and piezometers showed a statistically significant decreasing trend in pre-monsoon; and 36 observation wells and piezometers reflected a statistically significant increasing trend and 30 observation wells and piezometers reflected a statistically significant decreasing trend in monsoon. Interestingly, most of the observation wells and piezometers with increasing trends of DGWL were located in Dausa and Jaipur districts. Furthermore, the GAM analysis revealed that climatic variables, such as precipitation, significantly affected DGWL in Barmer District, and DGWL in all other districts was influenced by anthropogenic variables, including GDP, NIA, and population. As a result, stringent regulations should be implemented to curb excessive groundwater extraction, manage agricultural water demand, initiate proactive aquifer recharge programs, and strengthen sustainable management in these water-scarce regions.

The importance of valuing environmental resources, especially in ecotourism sites, has become increasingly important over the last two decades. Ecotourism is now considered as an important source of livelihood of local stakeholders in backward regions. Therefore, the preservation of ecotourism sites through community participation seems very important to maintain continued flow of tourists. This study aimed at recognizing the importance of community participation for the preservation of ecotourism sites. For this, this study executed a survey based on non-probability sampling in two ecotourism sites (Garpanchkot and Baranti) covering 100 respondents in Purulia District, West Bengal of India. The central issue of this study was to assess the tendency of community participation for the conservation of ecotourism sites and find the optimum condition for offering participatory labour time. This study showed that the participation of young people is high, and the majority of respondents are aware of the importance in protecting ecotourism sites. Because respondents were too poor to offer money, the contingent valuation method (CVM) was used to elicit their willingness to pay (WTP) participatory labour time for the conservation of ecotourism sites. Respondents’ age, income, education level, caste, and their perceived environmental quality had significant relationship with their WTP participatory labour time by applying the ordinary least square (OLS) model. It was found that the mean WTP participatory labour time of each respondent in a month is approximately 3.64 h. The significance of this study is that community participation can improve the sense of belonging, trust, and credibility of ecotourism sites, making them more appreciative of the value and protection of these sites.

As urbanization accelerates, rural regions in China are experiencing transformative changes. This study examines the transformation mechanism of modern agricultural villages in the loess hilly and gully regions, using Zhaojiawa Village in Shannxi Province of China as a case study. In this study, we explored the village’s evolution amid China’s rural revitalization efforts, highlighting the transition from a traditional agricultural village to a modern agricultural village in the context of rapid urbanization. This study employed actor-network theory (ANT) to investigate the complex interactions among diverse actors that drive rural transformation. ANT interlinks spatial relationships with intricate social networks. We utilized Google Earth remote sensing images in 2015 and 2021 and interview data to construct ANT. Three key dimensions of rural transformation are identified: economic structure transformation, social relationship reorganization, and spatial layout reconstruction. The transformation mechanism in Zhaojiawa Village is underpinned by a network of diverse actors, both human and non-human, aligned around two pivotal stages of agricultural village development (i.e., construction stage and development stage). In the initial construction stage, the Suide County government led a complex actor network to enhance rural living and production spaces. In the development stage, the village committee emerged as a central actor, with increased participation from villagers and external enterprises, facilitating the creation of a multifunctional space. The evolving goals and roles of these key actors contributed to the reconfiguration of the actor network, promoting rural transformation. These insights are applicable to other ecologically vulnerable and economically challenged rural areas in the loess hilly and gully regions, suggesting that collaboration among stakeholders can effectively facilitate the transition to specialized and integrated industries, thereby fostering rural revitalization.

Carbon emissions have become a critical concern in the global effort to combat climate change, with each country or region contributing differently based on its economic structures, energy sources, and industrial activities. The factors influencing carbon emissions vary across countries and sectors. This study examined the factors influencing CO₂ emissions in the 7 South American countries including Argentina, Brazil, Chile, Colombia, Ecuador, Peru, and Venezuela. We used the Seemingly Unrelated Regression (SUR) model to analyse the relationship of CO₂ emissions with gross domestic product (GDP), renewable energy use, urbanization, industrialization, international tourism, agricultural productivity, and forest area based on data from 2000 to 2022. According to the SUR model, we found that GDP and industrialization had a moderate positive effect on CO₂ emissions, whereas renewable energy use had a moderate negative effect on CO₂ emissions. International tourism generally had a positive impact on CO₂ emissions, while forest area tended to decrease CO₂ emissions. Different variables had different effects on CO₂ emissions in the 7 South American countries. In Argentina and Venezuela, GDP, international tourism, and agricultural productivity significantly affected CO₂ emissions. In Colombia, GDP and international tourism had a negative impact on CO₂ emissions. In Brazil, CO₂ emissions were primarily driven by GDP, while in Chile, Ecuador, and Peru, international tourism had a negative effect on CO₂ emissions. Overall, this study highlights the importance of country-specific strategies for reducing CO₂ emissions and emphasizes the varying roles of these driving factors in shaping environmental quality in the 7 South American countries.

A surging population in Karnataka State, a semi-arid region in India, poses a threat to both food security and livelihood sustainability, necessitating a concentrated effort to bolster agricultural efficiency and achieve United Naton’s Sustainable Development Goal 2 (zero hunger). Therefore, in order to address the pressing issue of food scarcity in Karnataka, this study meticulously examined the spatio-temporal variation of agricultural efficiency and irrigation intensity in Karnataka, uncovering its significant dependence of agricultural efficiency on irrigation intensity. Specifically, this study used a one-way analysis of variance (ANOVA) to ascertain significant differences in the means of agricultural efficiency and irrigation intensity during 2004-2005 and 2018-2019. This study showed that the maximum improvement in agricultural efficiency index was recorded in Belgaum (40.24), Gulbarga (24.77), and Yadgir districts (22.92) between 2004-2005 and 2018-2019, which indicated the progressing trend and better scope for agriculture extension. On the contrary, some districts expressed threat (a decline of above 20.00 of agricultural efficiency index) and needed special care for the improvement of agricultural efficiency in four northern districts (Bagalkot, Bidar, Raichur, and Bijapur), three southern districts (Chitradurga, Chikballapur and Hassan), and two southern districts (Koppal and Gadag) in Karnataka. During 2004-2005, irrigation intensity varied from 3.19% to 56.39%, with the lowest irrigation intensity in Kodagu District and the highest irrigation intensity in Shimoga District. During 2018-2019, irrigation intensity changed from 0.77% to 72.77%, with the lowest irrigation intensity in Kodagu District and the highest in Dakshin Kannad District. Moreover, the research scrutinized the complex relationship between agricultural efficiency and irrigation intensity, with the correlation coefficient increased from 0.162 during 2004-2005 to 0.255 during 2018-2019. It implies that in both periods, a low positive correlation existed between these two variables. Over time, several factors (high-yield seeds and chemical fertilizers) other than irrigation intensity gradually became essential for agricultural efficiency. This research offers a wealth of valuable insights for regional planners and policy-makers contending with comparable challenges in various regions of India and other developing countries.

Jeneberang watershed is vital, particularly for people living in Gowa Regency (South Sulawesi Province, Indonesia), who benefit from its many advantages. Landslides and floods occur every year in the Jeneberang watershed, so it is imperative to understand the socio-economic vulnerability of this region. This research aims to identify the vulnerability level of the Jeneberang watershed so that the government can prioritize areas with high vulnerability level and formulate effective strategies to reduce these the vulnerability. Specifically, this study was conducted in 12 districts located in the Jeneberang watershed. The primary data were collected from questionnaires completed by community members, community leaders, and various stakeholders, and the secondary data were from the Landsat satellite imagery in 2020, the Badan Push Statistic of Gowa Regency, and some governmental agencies. The socio-economic vulnerability variables were determined using the Multiple Criteria Decision Analysis (MCDA) method, and each variable was weighted and analyzed using the Geographical Information System (GIS). The study reveals that the levels of socio-economic vulnerability are affected by variables such as population density, vulnerable groups (disabled people, elderly people, and young people), road network and settlement, percentage of poor people, and productive land area in the Jeneberang watershed. Moreover, all of the 12 districts in the Jeneberang watershed are included in the medium vulnerability level, with the mean percentage of socio-economic vulnerability around 50.92%. The socio-economic vulnerability of Bajeng, Pallangga, and Somba Opu districts is categorized at high level, the socio-economic vulnerability of Bungaya, Parangloe, and Tombolo Pao districts is classified as medium level, and the remaining 6 districts (Barombong, Bontolempangan, Bontomarannu, Manuju, Parigi, and Tinggimoncong) are ranked as low socio-economic vulnerability. This study can help policy-makers to formulate strategy that contributes to the protection of biodiversity and sustainable development of the Jeneberang watershed, while improving disaster resilience and preparedness of the watershed.

The development of agro-industrial complex is important for ensuring national food security and national health. The development of rural areas is subject to the development of agriculture and local infrastructure, as well as the availability of various services. This study selected 15 indicators in 2021 to analyze the employment and development levels in rural areas of 71 regions of the Russian Federation using the analytical grouping method. The results indicated that 20 regions (Group 1) had the highest percentage of rural population (33.10%). The percentage of population engaged in agriculture had the highest value (12.40%) in 31 regions (Group 2). Moreover, 20 regions (Group 3) had the highest investments in fixed assets at the expense of municipal budget (11.80 USD/person). Increasing the investments in fixed assets carried out from the budget of the municipality can improve the employment level in rural areas. Then, we used cluster analysis to divide 14 regions of the Volga Federal District in the Russian Federation into 3 clusters. Cluster 1 covered Kirov Region and Republic of Mari El; Cluster 2 included Ulyanovsk Region, Saratov Region, Nizhny Novgorod Region, Perm Territory, Orenburg Region, Chuvash Region, and Republic of Mordovia; and Cluster 3 contained Republic of Tatarstan, Samara Region, Udmurtian Republic, Penza Region, and Republic of Bashkortostan. Results indicated that the 2 regions of Cluster 1 need to increase the availability of resources and natural gas and improve the investment attractiveness of rural areas. The 7 regions of Cluster 2 needed to develop infrastructure, public services, and agricultural production. We found the highest employment level in rural areas, the largest investments in fixed assets at the expense of municipal budget, the largest residential building area per 10,000 persons, and the largest individual residential building area in the 5 regions of Cluster 3. This study makes it possible to draw up a comprehensive regional development program and proves the need for the development of rural areas, which is especially important for the sustainable development of the Russian Federation.

Human well-being and livelihoods depend on natural ecosystem services (ESs). Following the increment of population, ESs have been deteriorated over time. Ultimately, land use/land cover (LULC) changes have a profound impact on the change of ecosystem. The primary goal of this study is to determine the impacts of LULC changes on ecosystem service values (ESVs) in the upper Gilgel Abbay watershed, Ethiopia. Changes in LULC types were studied using three Landsat images representing 1986, 2003, and 2021. The Landsat images were classified using a supervised image classification technique in Earth Resources Data Analysis System (ERDAS) Imagine 2014. We classified ESs in this study into four categories (including provisioning, regulating, supporting, and cultural services) based on global ES classification scheme. The adjusted ESV coefficient benefit approach was employed to measure the impacts of LULC changes on ESVs. Five LULC types were identified in this study, including cultivated land, forest, shrubland, grassland, and water body. The result revealed that the area of cultivated land accounted for 64.50%, 71.50%, and 61.50% of the total area in 1986, 2003, and 2021, respectively. The percentage of the total area covered by forest was 9.50%, 5.90%, and 14.80% in 1986, 2003, and 2021, respectively. Result revealed that the total ESV decreased from 7.42×10⁷ to 6.44×10⁷ USD between 1986 and 2003. This is due to the expansion of cultivated land at the expense of forest and shrubland. However, the total ESV increased from 6.44×10⁷ to 7.76×10⁷ USD during 2003-2021, because of the increment of forest and shrubland. The expansion of cultivated land and the reductions of forest and shrubland reduced most individual ESs during 1986-2003. Nevertheless, the increase in forest and shrubland at the expense of cultivated land enhanced many ESs during 2003-2021. Therefore, the findings suggest that appropriate land use practices should be scaled-up to sustainably maintain ESs.

There is a need for more focus in understanding the economic benefits of Climate-Smart Agriculture (CSA) interventions, particularly in sub-Saharan Africa, where extreme climate events are significantly affecting agriculture and rural livelihoods. This study used the Net Present Value (NPV), Internal Rate of Return (IRR), Benefit-Cost Ratio (BCR), and payback period to evaluate the economic viability of the adopted CSA interventions in the three villages (Doggoh, Jeffiri, and Wulling) of the dryland farming systems of northern Ghana, where CSA interventions were mostly practiced. Data were collected from 161 farm households by the questionnaire survey. The results showed that CSA interventions including livestock-crop integration, mixed cropping, crop rotation, nutrient integration, and tie ridging enhanced crop yield and the household income of smallholder farmers. The five CSA interventions selected by smallholders were in the following order of priority: livestock-crop integration (BCR=2.87), mixed cropping (BCR=2.54), crop rotation (BCR=2.24), nutrient integration (BCR=1.98), and tie ridging (BCR=1.42). Results further showed that livestock-crop integration was the most profitable CSA intervention even under a pessimistic assumption with a long payback period of 5.00 a. Moreover, this study indicated that the implementation of CSA interventions, on average, was relatively profitable and had a nominal financial risk for smallholder farmers. Understanding the economic viability of CSA interventions will help in decision-making process toward selecting the right CSA interventions for resilience development.

Higher education plays an important role in all aspects of sustainability. Infusing climate change and sustainability into the curriculum is important to prepare future health-care professionals for climate change and sustainability actions to build resilient health systems. In this study, we evaluated the preparedness, knowledge, and perception regarding climate change and its impact on human health among Indian nursing students. A quantitative descriptive survey was conducted among 644 undergraduate nursing students studying in three institutes in the southern, northern, and western parts of India. We used a structured online questionnaire with established validity and reliability to collect data. The data were analyzed using Statistical Package for the Social Sciences version 26 software. The results showed that more than half of the students reported experiencing excessive increase (362; 56.2%) and decrease (374; 58.1%) in temperature in recent years. Though more than three quarters of the students (494; 76.7%) accepted the general truth that climate change is mainly caused by human activities, only less than half of the students (309; 48.0%) perceived the impact of climate change to be high. Three quarters of the students (483; 75.0%) had a high level of knowledge regarding climate change. The students who had a high level of knowledge about climate change exerted a high perceived impact of climate change (χ²=75.47; P<0.01). More than half of the students (50.9%) felt that they needed information about climate change to be included in the nursing curriculum, and only one sixth of the students (16.1%) were highly confident about engaging in climate change-related conversation with patients. Most of the students who participated in the study had basic knowledge about climate change and its impact on human health. However, there is an observed gap between knowledge and their preparedness and confidence to engage in climate change actions. Hence, we propose to strengthen the nursing curriculum by integrating various curricular and co-curricular activities related to climate change, so that future nurses are empowered to become climate change advocates.

Human activities to improve the quality of life have accelerated the natural rate of soil erosion. In turn, these natural disasters have taken a great impact on humans. Human activities, particularly the conversion of vegetated land into agricultural land and built-up area, stand out as primary contributors to soil erosion. The present study investigated the risk of soil erosion in the Irga watershed located on the eastern fringe of the Chota Nagpur Plateau in Jharkhand, India, which is dominated by sandy loam and sandy clay loam soil with low soil organic carbon (SOC) content. The study used the Revised Universal Soil Loss Equation (RUSLE) and Geographical Information System (GIS) technique to determine the rate of soil erosion. The five parameters (rainfall-runoff erosivity (R) factor, soil erodibility (K) factor, slope length and steepness (LS) factor, cover-management (C) factor, and support practice (P) factor) of the RUSLE were applied to present a more accurate distribution characteristic of soil erosion in the Irga watershed. The result shows that the R factor is positively correlated with rainfall and follows the same distribution pattern as the rainfall. The K factor values in the northern part of the study area are relatively low, while they are relatively high in the southern part. The mean value of the LS factor is 2.74, which is low due to the flat terrain of the Irga watershed. There is a negative linear correlation between Normalized Difference Vegetation Index (NDVI) and the C factor, and the high values of the C factor are observed in places with low NDVI. The mean value of the P factor is 0.210, with a range from 0.000 to 1.000. After calculating all parameters, we obtained the average soil erosion rate of 1.43 t/(hm²•a), with the highest rate reaching as high as 32.71 t/(hm²•a). Therefore, the study area faces a low risk of soil erosion. However, preventative measures are essential to avoid future damage to productive and constructive activities caused by soil erosion. This study also identifies the spatial distribution of soil erosion rate, which will help policy-makers to implement targeted soil erosion control measures.

Climate change vulnerability assessment is an essential tool for identifying regions that are most susceptible to the impacts of climate change and designing effective adaptation actions that can reduce vulnerability and enhance long-term resilience of these regions. This study explored a framework for climate change vulnerability assessment in the new urban planning process in Jangwani Ward, Tanzania. Specifically, taking flood as an example, this study highlighted the steps and methods for climate change vulnerability assessment in the new urban planning process. In the study area, 95 households were selected and interviewed through purposeful sampling. Additionally, 10 respondents (4 females and 6 males) were interviewed for Focus Group Discussion (FGD), and 3 respondents (1 female and 2 males) were selected for Key Informant Interviews (KII) at the Ministry of Lands, Housing and Human Settlements Development. This study indicated that climate change vulnerability assessment framework involves the assessment of climatic hazards, risk elements, and adaptive capacity, and the determination of vulnerability levels. The average hazard risk rating of flood was 2.3. Socioeconomic and livelihood activities and physical infrastructures both had the average risk element rating of 3.0, and ecosystems had the average risk element rating of 2.9. Adaptive capacity ratings of knowledge, technology, economy or finance, and institution were 1.6, 1.9, 1.4, and 2.2, respectively. The vulnerability levels of socioeconomic and livelihood activities and physical infrastructure were very high (4.0). Ecosystems had a high vulnerability level (3.8) to flood. The very high vulnerability level of socioeconomic and livelihood activities was driven by high exposure and sensitivity to risk elements and low adaptive capacity. The study recommends adoption of the new urban planning process including preparation, planning, implementation, and monitoring-evaluation-review phases that integrates climate change vulnerability assessment in all phases.

The current linear economy assumes abundant, easily accessible, and cost-effective natural resources. However, this assumption is unsustainable, especially considering the world’s current trajectory exceeding the Earth’s ecological limits. In contrast, circular economy (CE) reduces wastes and improves resource efficiency, making them a more sustainable alternative to the dominant linear model. Biomass energy generated from agricultural leftovers, forestry wastes, and municipal trash provides a renewable substitute for fossil fuels. This reduces greenhouse gas emissions and improves energy security. Proper waste management, including trash reduction, recycling, and innovative waste-to-energy technology, reduces the burden on landfills and incineration and creates renewable energy from materials that would otherwise go to waste. Although integrating these techniques is consistent with the CE’s resource efficiency and waste minimization principles, it requires addressing environmental, technical, and socioeconomic challenges. Given the pressing global issues, transitioning to a CE and implementing sustainable environmental practices are crucial to mitigate the current waste management crisis. The aim of this study is to emphasize the viability of biomass as a source of sustainable energy, the necessity of comprehensive strategies that prioritize ecological sustainability, community involvement, and innovation to achieve a circular principle based future, and the potential obstacles to the implementation of sustainable environmental practices. This study will aid in implementing CE practices to accomplish the Sustainable Development Goals (SDGs) by reducing greenhouse gas emissions and landfill loads. Beyond environmental benefits, it can also bring economic, social, and health improvements. Furthermore, this study will assist societies in addressing global issues, such as resource scarcity, pollution, and climate change, as well as transitioning to a more sustainable and resilient future.

Coordinative development across various systems, particularly the economic, social, cultural, and human resources subsystems, is a key aspect of urban sustainability and has a direct impact on the quality of urbanization. The Hangzhou Metropolitan Circle, comprising Hangzhou City, Huzhou City, Jiaxing City, and Shaoxing City, was the first metropolitan circle approved by the National Development and Reform Commission (NDRC) as a demonstration of economic transformation in China. To evaluate the coupling coordination degree of the four cities and analyze the coordinative development in three systems (including digital economy, regional innovation, and talent employment), we collected panel data during 2015-2022 from these four cities. The development level of the three systems was evaluated by the standard deviation method and comprehensive development index. The results are as follows: (1) the level of coupling coordinated development of the three systems in the Hangzhou Metropolitan Circle was relatively low; (2) the coupling coordination degree of the four cities in the Hangzhou Metropolitan Circle showed significant regional differences, among which Hangzhou City was in the leading position, and Huzhou, Jiaxing, and Shaoxing cities made steady but slow progress in the coupling development of the three systems; and (3) the development of digital economy and talent employment needs to be strengthened. This study contributes to the coordinative development of Hangzhou Metropolitan Circle by innovatively focusing on the coupling coordination relationship among digital economy, regional innovation, and talent employment, which also meets the industrial layout of Hangzhou Metropolitan Circle. In this way, the optimal allocation and sustainable development of digital economy, regional innovation, and talent employment in the Hangzhou Metropolitan Circle can be achieved.

With the population growth through natural growth and migration, coupled with the city expansion, it is the fact that Dehradun City in India faces severe water scarcity. Therefore, the Song Dam Drinking Water Project (SDDWP) is proposed to provide ample drinking water to Dehradun City and its suburban areas. This paper examined economic significance and environmental impacts of the SDDWP in Garhwal Himalaya, India. To conduct this study, we collected data from both primary and secondary sources. There are 12 villages and 3 forest divisions in the surrounding areas of the proposed dam project, of which 3 villages will be fully submerged and 50 households will be affected. For this study, 50 heads of the households were interviewed in the 3 submerged villages. The questions mainly focused on economic significance, environmental impacts, and rehabilitation issues of the dam project. The findings of this study indicate that economic significance of the dam project is substantial, including providing ample water for drinking and irrigation, contributing to groundwater recharge, creating job opportunities, and promoting the development of tourism and fisheries in the Doon Valley. In terms of the rehabilitation of the affected people, there are only 50 households in need of rehabilitation. Currently, the arable land of these affected people is not sufficient to sustain their livelihoods. The entire landscape is fragile, rugged, and precipitous; therefore, the affected people are willing to rehabilitate to more suitable areas in the Doon Valley. Moreover, it is essential to provide them with sufficient compensation packages including the compensation of arable land, houses, cash, common property resources, institutions, belongingness, and cultural adaptation. On the other hand, the proposed dam project will have adverse environmental impacts including arable land degradation, forest degradation, loss of fauna and flora, soil erosion, landslides, and soil siltation. These impacts will lead to the ecological imbalances in both upstream and downstream areas. This study suggests that the affected people should be given sufficient compensation packages in all respects. Afforestation programs can be launched in the degraded areas to compensate for the loss of forest in the affected areas.

Indigenous people around the Kerinci Seblat National Park (KSNP), Indonesia, have a high dependence on forest resources as their main source of livelihood. This study addressed the vulnerability of Rejang indigenous people around the KSNP to climate change. The popular livelihood vulnerability index (LVI) model was adapted by adding and modifying subcomponents suitable for the study area. Primary data were collected through household surveys in two communities: Embong and Topos. In total, 146 samples were selected for this study using stratified random sampling. The results showed that Embong was more vulnerable to climate change than Topos. Embong exhibited a higher level of vulnerability to the effects of socio-demographic profile, social network, health, and natural disasters and climate variability, whereas Topos was more vulnerable to livelihood strategy, food, and water. Furthermore, Embong was more exposed to natural disasters and climate variability than Topos, but it demonstrated higher adaptive capacity and lower sensitivity than Topos. Nevertheless, socio-demographic profile influenced adaptive capacity in both communities. Sensitivity was influenced most by health in Embong, and sensitivity was influenced most by food in Topos. Although the vulnerability levels were not high in the two communities, several subcomponents must be specifically considered. Overall, this study can help the government make informed decisions to enhance adaptive capacity of the KSNP to climate change.

The existing approaches for the design of tourist areas often lead to limited flexibility in project implementation. To realize a more flexible approach, in this study, we formulated a model for planning and designing tourist areas at the local level. Moreover, specific tools for analyzing tourist areas and ensuring sustainable development under changing conditions were developed. This study was conducted in two tourist regions, Biryuzovaya Katun tourist complex and Belokurikha destination (including Belokurikha City with Belokurikha 2 Gornaya tourist complex and surrounding areas), in the Altay Mountains. We employed the recreation opportunity spectrum and proposed a system-integrated programming approach for the design of tourist areas at the destination and site levels. The key of this approach was the collection and analysis of current spatial data, including the spatial distribution of attractions and visitor flows. We constructed heat maps using video recording and unmanned aerial vehicle (UAV) observation data. Moreover, we analyzed the video stream using an image-analyzing framework You Only Look Once (YOLO) v5 software. The heat map of visitor flows based on video recording data in the Andreevskaya Sloboda museum of Belokurikha 2 Gornaya tourist complex allowed us to highlight the most attractive sites in this area and classify them into one of three types: points of functional concentration, points of transitional concentration, and points of attractions. The heat maps in Biryuzovaya Katun tourist complex, created using UAV observation data, allowed us to determine the spatiotemporal patterns of visitor flows and tourists’ preferences throughout the day within four time intervals: 09:00-12:00, 13:00-14:00, 14:00-15:00, and 16:00-18:00 (LST). The maximum visitor flow density occurred from 16:00 to 18:00 in the beach area of the artificial lake. A comparison between the visitor-concentrated sites and the current facilities provided insights into the demand for attractions and facilities and the lacking areas. Heat maps are useful in analyzing the land use at the site level, while zoning based on the recreation opportunity spectrum can be used to design tourist areas at the destination level. The proposed methods for analyzing the use of tourist areas contribute to the development of adaptive tourism design.

There is a gradual increase in the proportion of renewable energy sources. Green hydrogen has the potential to become one of the major energy carriers in the future. The Russian Federation, in partnership with countries in the Asia-Pacific region and especially China, has the potential to play a significant role in green hydrogen market. This study assessed the potential of developing green hydrogen energy based on wind power in the Far Eastern Federal District (FEFD) of the Russian Federation. Empirical wind speed data were collected from 20 meteorological stations in 4 regions (Sakhalinskaya Oblast’, Primorskiy Krai, Khabarovskiy Krai, and Amurskaya Oblast’) of the FEFD. The Weibull distribution was used to predict the potential of green hydrogen production. Five different methods (Empirical Method of Justus (EMJ), Empirical Method of Lysen (EML), Maximum Likelihood Method (MLE), Power Density Method (PDM), and Median and Quartiles Method (MQM)) were used to determine the parameters (scape factor and scale factor) of the Weibull distribution. We calculated the total electricity generation potential based on the technical specifications of the three wind turbines: Senvion 6150 onshore, H165-4.0 MW, and Vestas V150-4.2 MW. The results showed that Vladivostok, Pogibi, Ilyinskiy, Yuzhno-Kuril’sk, Severo-Kuril’sk, Kholmsk, and Okha stations had the higher potential of green hydrogen production, of which Vladivostok exhibited the highest potential of green hydrogen production using the wind turbine of H165-4.0 MW, up to 2.56×10⁵ kg/a. In terms of economic analysis, the levelized cost of hydrogen (LCOH) values of lower than 4.00 USD/kg were obtained at Yuzhno-Kuril’sk, Ilyinskiy, Pogibi, and Vladivostok stations using the wind turbine of H165-4.0 MW, with the values of 3.54, 3.50, 3.24, and 2.55 USD/kg, respectively. This study concluded that the FEFD possesses significant potential in the production of green hydrogen and, with appropriate investment, has the potential to become a significant hub for green hydrogen trading in the Asia-Pacific region.

Community-supported agriculture (CSA) has emerged as a viable solution for addressing the agricultural challenges faced by countries like Indonesia. This study uses the well-established unified theory of acceptance and use of technology (UTAUT2) model to examine the interest in CSA of potential customers in Indonesia. A standardized questionnaire was distributed to 1200 respondents, and the data were analyzed using structural equation model-partial least square (SEM-PLS) in SmartPLS 4.0 software. The results capture potential CSA consumer interest and will help to improve CSA development strategies in Indonesia. The model explains 44.4% of customers’ intentions, and identifies performance expectancy as the decisive factor in customers’ willingness to participate in CSA. Performance expectancy (0.292), hedonic motivation (0.262), social influence (0.259), and facilitating conditions (0.086) positively influence customers’ interest in participating in a CSA program. The adoption of CSA programs by both farmers and customers could be increased by implementing regulations that provide tax incentives and subsidies, offering training on sustainable farming practices, facilitating the establishment of distribution channels, and establishing guidelines for fair price and quality standards. This study shows the high potential for the implementation of CSA in Indonesia. It could also be used as a foundation for the development of new policies regarding sustainable agriculture markets in Indonesia.

Climate change poses substantial challenges to agricultural productivity and sustainability, particularly in Mediterranean and Sub-Saharan Africa regions. Local smallholder farmers’ adaptation strategies to climate change are crucial for mitigating these impacts. Therefore, this study investigated the socioeconomic factors influencing smallholder farmers’ perceptions and adaptation strategies to climate change in four countries (Morocco, Egypt, Italy, and Senegal) of Mediterranean and Sub-Saharan Africa regions using a binary logistic regression (BLR) model. The results indicated that educational level, farming experience, agricultural income, farm size, participation in agricultural workshops, and training in Good Agricultural Practices (GAPs) significantly impacted smallholder farmers’ perceptions and adaptation strategies to climate change (such as smallholder farmers adopting drought-tolerant crops). Higher educational level was linked to the greater possibility of smallholder farmers adopting drought-tolerant crops in Italy and Egypt, while gaps in rural education limited the possibility of smallholder farmers adopting drought-tolerant crops in Morocco and Senegal. Farming experience and agricultural income also enhanced the possibility of smallholder farmers adopting drought-tolerant crops, with notable variations across countries due to systemic barriers such as limited infrastructure in Senegal. Larger farm size and participation in agricultural workshops further improved the possibility of smallholder farmers adopting drought-tolerant crops, particularly in Morocco and Egypt. The findings highlighted the importance of tailored interventions and policy measures to support smallholder farmers in effectively responding to the challenges of climate change under diverse agricultural contexts. By understanding the specific needs and circumstances of smallholder farmers in these countries, policymakers can develop more effective adaptation strategies to enhance agricultural resilience and sustainability under the context of climate change.

Rural areas are crucial for a country’s sustainable economy. New strategies are needed to develop rural areas to improve the well-being of rural population and generate new job opportunities. This is especially important in countries where agricultural production accounts for a significant share of the gross product, such as Russia. In this study, we identified the key indicators of satisfaction and differences between rural and urban citizens based on their social, economic, and environmental backgrounds, and determined whether there are well-being disparities between rural and urban areas in the Stavropol Territory, Russia. We collected primary data through a survey based on the European Social Survey framework to investigate the potential differences between rural and urban areas. By computing the regional well-being index using principal component analysis, we found that there was no statistically significant difference in well-being between rural and urban areas. Results of key indicators showed that rural residents felt psychologically more comfortable and safer, assessed their family relationships better, and adhered more to traditions and customs. However, urban residents showed better economic and social conditions (e.g., infrastructures, medical care, education, and Internet access). The results of this study imply that we can better understand the local needs, advantages, and unique qualities, thereby gaining insight into the effectiveness of government programs. Policy-makers and local authorities can consider targeted interventions based on the findings of this study and strive to enhance the well-being of both urban and rural residents.

Land use conflicts (LUCs), as a spatial manifestation of the conflicts in the human-land relationships, have a profound impact on regional sustainable development. For China’s metropolitan junction areas (MJAs), the existence of “administrative district economies” has made the issue of LUCs more prominent. Based on a case study of the central Chengdu-Chongqing region, we conducted an exploratory spatial data analysis of the evolutionary process of regional LUCs. Furthermore, structural equation modeling was utilized to analyze the dynamic mechanism of LUCs in MJAs, with a particular emphasis on exploring the influences of administrative boundary. The results showed that from 2010 to 2020, LUCs in the central Chengdu-Chongqing region continued to worsen, and the spatial process conflict and spatial structure conflict indices increased by more than 30.0%. The intensification of LUCs in the central Chengdu-Chongqing region from 2010 to 2020 was mainly the result of the deterioration of conflicts in evaluation units with low conflict levels. LUCs in China’s metropolitan areas generally presented a circular gradient distribution, weakening from the core to the periphery, but there were some strong isolated conflict zones in the outer regions. LUCs in China’s MJAs were the result of interactions among multiple factors, e.g., natural environment, socio-economic development, policy and institutional processes, and administrative boundary effects. Administrative boundary affected the flow of socio-economic elements, changing the supply-and-demand competition of stakeholders for land resources, consequently exerting an indirect influence on LUCs. This study advances the theory of the dynamic mechanism of LUCs, and provides theoretical support for the governance of these conflicts in transboundary areas.

Forest resources play a vital role in supporting the livelihoods of rural communities residing in forest-rich areas. In India, a forest-rich country, a significant proportion of non-timber forest products (NTFPs) is consumed locally, supporting numerous rural communities relying on forests for essential resources, such as firewood, timber, and NTFPs. This study focuses on two forest-dominant districts in West Bengal of India, namely, Jhargram District and Paschim Medinipur District. Furthermore, this study aims to enhance the understanding of forest-dependent communities by comparing the standard of living among different village classes. Thus, we categorized villages into three classes based on the distance from home to forests, including inner villages, fringe villages, and outer villages. Through focus group discussions and household surveys, we explored the sources of local economy, income sources of household, and reasons for economic diversification in different village classes. The study findings confirm that substantial variations existed in the income sources and the standard of living in these villages. Forest income varied dramatically among the three village classes, with inner villages having greater forest income than fringe villages and outer villages. Meanwhile, households in outer villages depended on forests and engaged in diverse economic activities for their livelihoods. Compared with inner and fringe villages, households in outer villages derived a significant portion of their income from livestock. This discrepancy can be attributed to challenges, such as inadequate transportation, communication, and underdeveloped market chains in inner villages. Moreover, these findings emphasize the need to develop sustainable forest management practices, create alternative income-generation opportunities, and improve infrastructure and market access in inner villages, as well as promote economic diversification in outer villages. Through targeted policy measures, these forest-rich regions can achieve improved livelihoods, enhanced standard of living, and increased resilience for their communities.

Historically, geopolitical risk (GPR) has posed significant challenges to international economic, social, and political frameworks. This study investigated how internal GPR in the selected five Southeast Asian countries (Indonesia, South Korea, Malaysia, the Philippines, and Thailand) influences foreign direct investment (FDI) during 1996-2019. The stationarity of the data was assessed using the Augmented Dickey-Fuller (ADF) unit root test, which shows that the data became stationary after the first difference. The Kao, Pedroni, and Westerlund cointegration tests were employed to examine long-term cointegration among the selected variables (FDI, GPR index (GPRI), gross domestic product (GDP), inflation, interest rate, and trade openness (TOP)). The results indicated that these variables have a long-term cointegration. Consequently, regression analysis using the Pooled Ordinary Least Squares (OLS) regression, fixed effect, random effect, Arellano-Bond dynamic panel-data estimation, and system generalized moment method (GMM) revealed that GPRI and TOP negatively impacted FDI in the selected five Southeast Asian countries. At the same time, GDP, inflation, and interest rate positively influenced FDI in these countries. Because FDI is crucial to shaping a country’s macroeconomic structure, this study recommends that governments and central banks of the selected five Southeast Asian countries should implement policies and strategies to encourage foreign investments.

Climate change studies are diverse with no single study giving a comprehensive review of climate change impacts, adaptation strategies, and policy development in West Africa. The unavailability of an all-inclusive study to serve as a guide for practitioners affects the effectiveness of climate change adaptation strategies proposed and adopted in the West African sub-region. The purpose of this study was to review the impacts of climate change risks on the crop, fishery, and livestock sectors, as well as the climate change adaptation strategies and climate-related policies aimed at helping to build resilient agricultural production systems in West Africa. The review process followed a series of rigorous stages until the final selection of 56 articles published from 2009 to 2023. Generally, the results highlighted the adverse effects of climate change risks on food security. We found a continuous decline in food crop production. Additionally, the livestock sector experienced morbidity and mortality, as well as reduction in meat and milk production. The fishery sector recorded loss of fingerlings, reduction in fish stocks, and destruction of mariculture and aquaculture. In West Africa, climate-smart agriculture technologies, physical protection of fishing, and inclusion of gender perspectives in programs appear to be the major adaptation strategies. The study therefore recommends the inclusion of ecosystem and biodiversity restoration, weather insurance, replacement of unsafe vessels, and strengthening gender equality in all climate change mitigation programs, as these will help to secure enough food for present and future generations.

The rapid acceleration of global warming and intensifying human activities have exacerbated the fragility and climate sensitivity of ecosystems worldwide, particularly in arid regions. Vegetation, a key component of ecosystems, is critical in enhancing the ecological environment. The Ertix River Basin (ERB) is a transboundary watershed that spans multiple countries, mostly in arid regions. However, research on the fractional vegetation coverage (FVC) and its driving factors in the ERB remains limited. Investigating the spatiotemporal changes in the FVC and its relationship with various factors in the ERB can offer scientific support for optimizing regional vegetation restoration policies and promoting the coordinated development of human-environment interactions. The Moderate-resolution Imaging Spectroradiometer (MODIS) MYD13Q1 V6 data were obtained via the Google Earth Engine platform, and methods including the pixel dichotomy method, Theil-Sen median trend analysis, and Mann‒Kendall test were employed to examine the spatiotemporal dynamics of the FVC in the ERB from 2003 to 2023, with future trend forecast using the Hurst index. The impacts of natural and socioeconomic factors on the FVC were evaluated through the partial least squares-structural equation model (PLS-SEM). The results indicated that the FVC in the ERB showed a slight degradation trend with an average annual decrease of 0.046% during 2003-2023, with significant changes occurring in 2004, 2010, and 2019. Spatially, 53.380% of the study area was degraded, and the change in the FVC increased gradually from southeast to northwest. The FVC in 63.000% of the study area was highly stable and displayed long-term persistence; and the direct impact of natural factors (path coefficient of 0.617) on the FVC was significantly higher than that of socioeconomic factors (0.167). Among the natural factors, precipitation (0.999) was the most significant. This study reveals the significant impacts of natural and socioeconomic factors on vegetation dynamics in arid regions, and provides a scientific basis for transnational ecological conservation.