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.
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 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.
Poverty remains one of the most pressing global challenges of this era, affecting millions of people across both developing and developed countries. The poverty alleviation resettlement (PAR) is a policy with Chinese characteristics for eradicating poverty. By integrating the Maslow’s Hierarchy of Needs and Amartya Sen’s Capability Approach, this study developed a theoretical framework to analyze the factors influencing the well-being of poverty alleviation migrants (PAMs). A telephone survey conducted between July and August 2022 in Hubei Province, Guizhou Province, Shaanxi Province, and Chongqing Municipality of China yielded 259 valid questionnaires. Using the partial least squares-structural equation modeling (PLS-SEM), this study revealed that financial accessibility, health level, living conditions, and social networks significantly enhanced the well-being of PAMs, with living conditions having the strongest impact on the well-being of PAMs. Furthermore, the factors affecting well-being varied across age groups. Social networks played a more significant role in the elderly group, whereas health level had a greater impact on the young and middle-aged group. These findings deepen the understanding of the PAR and its effects on the well-being of PAMs, offering valuable insights for policy-makers and practitioners to refine poverty alleviation strategies and enhance social welfare.
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.
The social progress index (SPI) measures social and environmental performance beyond traditional economic indicators, providing transparent and actionable insights into the true condition of societies. This study investigates the interdependencies among SPI components and their impact on country-level sustainability performance. Using a Bayesian Belief Network (BBN) approach, the analysis explores the interdependencies among 12 SPI components (including advanced education, basic education, environmental quality, freedom and choice, health, housing, inclusive society, information and communications, nutrition and medical care, rights and voice, safety, and water and sanitation) and their collective influence on sustainability performance. Data from the Sustainable Development Report and SPI datasets, covering 162 countries (including Australia, China, United Arab Emirates, United Kingdom, United States, and so on), were used to assess the relative importance of each SPI component. The key findings indicate that advanced education, inclusive society, and freedom and choice make substantial contributions to high sustainability performance, whereas deficiencies in nutrition and medical care, water and sanitation, and freedom and choice are associated with poor sustainability performance. The results reveal that sustainability performance is shaped by a network of interlinked SPI components, with education and inclusion emerging as key levers for progress. The study emphasizes that targeted improvements in specific SPI components can significantly enhance a country’s overall sustainability performance. Rather than visualizing countries’ progress through composite indicator-based heat maps, this study explores the interdependencies among SPI components and their role in sustainability performance at the global level. The study underscores the importance of a multidimensional policy approach that addresses social and environmental factors to enhance sustainability. The findings contribute to a deeper understanding of how SPI components interact and shape sustainable development.
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.
