Socio-economic Drivers Research Articles

Role of tree loggers in promoting insecurity in Sabon Birni Local Government Area, Sokoto State: A review

This study examines the relationship between tree logging, deforestation, and insecurity in Sabon Birini, Sokoto State, Nigeria. The paper reviews existing literature on the environmental and socio-economic impacts of logging and deforestation, highlighting their contributions to insecurity in the region. The findings reveal that illegal logging and deforestation have led to environmental degradation, loss of biodiversity, and increased greenhouse gas emissions. Furthermore, the study shows that the socio-economic drivers of insecurity, including poverty and unemployment, are exacerbated by the loss of forest resources. The study recommends localized research, community-based approaches, education and awareness programs, policy reforms, and support for alternative livelihoods to address the dual challenges of deforestation and insecurity in Sabon Birini. The paper concludes that addressing the dual challenges of deforestation and insecurity requires a multi-faceted approach that involves localized research, community-based interventions, and education and awareness programs

Analysis of Spatiotemporal Dynamics and Driving Factors of China’s Nationally Important Agricultural Heritage Systems

China’s Nationally Important Agricultural Heritage Systems (China-NIAHS) are agricultural systems with deep historical and cultural roots that exhibit temporal continuity and spatial heterogeneity in their formation and distribution. As modern and industrialized agriculture have developed, traditional agricultural systems are facing unprecedented challenges and pressures. This study investigates the spatiotemporal distribution and influencing factors of 196 China-NIAHS sites, categorized into five categories. Using spatial analysis techniques and Geographical Detectors, this study identifies key natural, socioeconomic, and cultural drivers shaping their distribution. The results reveal a predominantly clustered spatial distribution of China-NIAHS, centered around the Yangtze River Basin, with significant influences from population density, tourism development, and industrialization. Historical analysis highlights a west-to-east and northward migration of agricultural activity, driven by political stability and technological advancements. Further findings indicate that the spatial distribution of China-NIAHS is primarily determined by population density, tourism development, and river network density. Population density plays a pivotal role in heritage preservation, tourism development generates economic benefits and facilitates cultural dissemination, and river network density supports the formation and sustainability of heritage sites. Conversely, urbanization and economic development have limited influence, emphasizing the need to prioritize socioeconomic and natural factors in conservation strategies. This study provides a comprehensive understanding of the spatial and temporal dynamics of China-NIAHS, offering valuable insights for sustainable heritage conservation and the strategic integration of natural and socioeconomic factors into modern agricultural policies. These findings deepen the understanding of China-NIAHS, highlighting their role in ecological and cultural sustainability while supporting value assessment, region-specific protection, and sustainable utilization strategies.

Drivers analysis and future scenario-based predictions of nutrient loads in key lakes and reservoirs of the Yangtze River Catchment.

The excessive nutrient loading in lakes and reservoirs poses significant threats to water quality and ecological health, especially under the influence of global climate change and intensified human activities. This study focuses on the long-term trends in nutrient content and ratios, as well as their driving factors in six major lakes and reservoirs (Chaohu Lake, Danjiangkou Reservoir, Dianchi Lake, Dongtinghu Lake, Poyanghu Lake, and Taihu Lake) within the Yangtze River Catchment from 2002 to 2021. Utilizing Redundancy Analysis, Random Forest and Generalized Additive Model, we identify the shifts in natural and socio-economic factors influencing nutrient concentrations and predict future trends under various scenarios. The main driving factors of nutrient content and their ratios have undergone significant changes at different historical stages, with livestock poultry breeding (LPB) and hydraulic retention time (Res_time) being consistently influential. Our findings highlight the dominant role of livestock and poultry breeding and hydraulic retention time in shaping TN content, whereas TP levels are significantly affected by both natural factors (Temperature and Rainfall) and socio-economic activities. Scenario analyses reveal that despite improvements in water management, nutrient loads remain high, posing ongoing risks of eutrophication. Future lakes nutrient content can meet existing water quality standards under socio-economic development scenarios that significantly reduce hydraulic retention time and the contributions of livestock poultry breeding and other socio-economic drivers.

Global Crop-Specific Fertilization Dataset from 1961–2019

As global fertilizer application rates increase, high-quality datasets are paramount for comprehensive analyses to support informed decision-making and policy formulation in crucial areas such as food security or climate change. This study aims to fill existing data gaps by employing two machine learning models, eXtreme Gradient Boosting and HistGradientBoosting algorithms to produce precise country-level predictions of nitrogen (N), phosphorus pentoxide (P2O5), and potassium oxide (K2O) application rates. Subsequently, we created a comprehensive dataset of 5-arcmin resolution maps depicting the application rates of each fertilizer for 13 major crop groups from 1961 to 2019. The predictions were validated by both comparing with existing databases and by assessing the drivers of fertilizer application rates using the model’s SHapley Additive exPlanations. This extensive dataset is poised to be a valuable resource for assessing fertilization trends, identifying the socioeconomic, agricultural, and environmental drivers of fertilizer application rates, and serving as an input for various applications, including environmental modeling, causal analysis, fertilizer price predictions, and forecasting.

Experience-based food insecurity in Bangladesh: Evidence from Household Income and Expenditure Survey 2022.

This paper examines the current state of food insecurity in Bangladesh and its socio-economic drivers using data from the latest Household Income and Expenditure Survey (HIES 2022). Unlike previous studies that relied on less precise measures of food insecurity, such as food expenditure, diversity, and calorie intake, this study employs the internationally recognized Food Insecurity Experience Scale (FIES) and Rasch model-based thresholds to classify households as food secure or insecure. Multilevel logistic regression is used to identify significant predictors of moderate and severe food insecurity, considering the hierarchical structure of the data, with households nested within geographical clusters. Key factors found to be significantly associated with food security include the wealth index, land ownership, education of the household head, family size, remittance income and exposure to shocks. A classification tree, a popular machine learning method, is also applied to explore important interactions among these determinants. The tree analysis confirms the importance of several regression-based predictors and identifies households at the highest risk of food insecurity through variable interactions. Factors such as poverty, lack of land ownership, low education levels, and high dependency ratios collectively increase a household's vulnerability to moderate food insecurity to around 51% while the national prevalence is 19%. District-level maps of food insecurity prevalence reveal significant regional disparities, underscoring the need for targeted, district-specific interventions to effectively combat food insecurity. More broadly, policies promoting education and family planning, training in better shock management, and facilitating remittance flows through simplified processes may contribute to addressing the food insecurity challenge.

Evaluating the socio-economic drivers of household adoption of biodigester systems for domestic energy in rural Colombia

Evaluating the socio-economic drivers of household adoption of biodigester systems for domestic energy in rural Colombia

Addressing Socio-Economic Drivers of Management Style Evolution: Embracing Complexity Leadership Solutions

Addressing Socio-Economic Drivers of Management Style Evolution: Embracing Complexity Leadership Solutions

Law Enforcement's Role in Tackling Illegal Gold Mining for Sustainable Development Goals

Illegal gold mining is a significant environmental crime, particularly in resource-rich regions like the Amazon, West Africa, and Southeast Asia. This study uses a qualitative research design to evaluate law enforcement efforts in Brazil, Ghana, and Indonesia. Primary data were collected through semi-structured interviews involving law enforcement personnel, government officials, environmental experts, and local community members affected by mining activities. These interviews aimed to uncover challenges in enforcing environmental laws, the effectiveness of ongoing measures, and local perceptions of enforcement outcomes. Secondary data comprised an extensive academic literature review, official reports, and legal documents to contextualize the findings within global trends. Case studies were selected based on criteria such as the prevalence of illegal gold mining, environmental impacts, and variations in enforcement strategies. Thematic analysis was employed to identify recurring issues such as corruption, resource constraints, and lack of political will. Results indicate that enforcement has had limited success in curbing deforestation, mercury contamination, and biodiversity loss, mainly due to institutional weaknesses. Enforcement measures disproportionately target small-scale miners while larger organized networks evade prosecution. This paper emphasizes the importance of addressing socioeconomic drivers through alternative livelihoods, international collaboration, and enhanced institutional capacity to achieve sustainable development goals.

Dynamic Methane Emissions from China's Fossil-Fuel and Food Systems: Socioeconomic Drivers and Policy Optimization Strategies.

In response to the 2023 "Action Plan for Methane Emission Control" in China, which mandates precise methane (CH4) emission accounting, we developed a dynamic model to estimate CH4 emissions from fossil-fuel and food systems in China for the period 1990-2020. We also analyzed their socioeconomic drivers through the Logarithmic Mean Divisia Index (LMDI) model. Our analysis revealed an accelerated emission increase (850.4 Gg/year) during 2005-2015, compared to 570.4 Gg/year in the preceding period (1990-2005), with a downward trend (-1216.6 Gg/year) detected after 2015. The fossil-fuel system was the primary contributor to these changes, with emissions positively correlated with per capita GDP and negatively influenced by energy intensity at the production stage and wastewater discharge intensity at the disposal stage. In the food system, CH4 emission intensity and waste treatment practices were the most significant negative drivers at production and disposal stages, respectively. Urbanization also played a notable role, contributing to 19.3% and 18.1% in livestock and rice cultivation emission reductions, respectively. Despite the observed changes, coal mining, livestock, and rice remain the dominant sources of CH4 emissions. Our findings suggest that effective CH4 emission mitigation can be achieved through strategies such as reducing energy intensity, improving agricultural production efficiency, and advancing urbanization efforts.

A Comprehensive and Innovative Environmental PSR Model for Biodiversity Priority Conservation Areas

Biodiversity is essential for ecosystem resilience and human well-being, yet it faces accelerating threats from habitat loss, climate change, and human activities. Conservation models often inadequately address the intertwined ecological and socio-economic drivers of biodiversity loss, leaving a gap between theoretical frameworks and real-world implementation. This study introduces an advanced Pressure-State-Response (PSR) model, developed through extensive fieldwork and leveraging Geographic Information Systems (GIS) and remote sensing technologies. The model integrates ecological indicators with socio-economic factors, including stakeholder engagement, education, and local economic conditions, creating a dynamic, context-specific approach to conservation. By adopting a Multi-Criteria Decision Analysis (MCDA) framework, specifically the Analytic Hierarchy Process (AHP), the enhanced PSR model prioritizes biodiversity hotspots based on ecological urgency and socio-economic resilience. It overcomes limitations of traditional models by incorporating customizable criteria and fostering equitable conservation strategies. The approach optimizes resource allocation, ensuring interventions target areas of highest biodiversity value while balancing local development needs. This study provides a replicable and adaptable methodology for conservation planning, addressing 21st-century challenges of biodiversity loss and socio-ecological complexity. By aligning conservation priorities with sustainable development goals, the model advances a transformative framework that bridges science, policy, and practice, offering global applicability for safeguarding biodiversity and ecosystem services.

Evolution and Attribution Analysis of Habitat Quality in China’s First Batch of National Parks

In October 2021, China established its first group of national parks, representing a milestone in enhancing the country’s nature reserve system and aligning with global trends in ecological conservation. This study aims to assess habitat quality changes and identify the driving factors in five national parks using multi-temporal land use data from 2000 to 2020. By integrating the land use transfer matrix with the InVEST model, we quantified habitat quality changes, while the geographical detector method was employed to analyze the key natural and socioeconomic drivers. Results showed that grassland and cultivated land were predominantly converted into forestland, leading to improvements in habitat quality in some parks. Specifically, Wuyishan National Park exhibited the highest and most stable habitat quality index, while Three-River-Source National Park experienced significant improvement (+34.10%). However, the Giant Panda, Northeast China Tiger and Leopard, and Hainan Tropical Rainforest National Parks experienced habitat degradation, with decreases of 15.15%, 14.50%, and 13.90%, respectively. Key drivers, such as NDVI, temperature, precipitation, elevation, and population density, were found to significantly influence habitat quality across the parks. This study highlights the ecological benefits of forestland restoration and the risks posed by the conversion of forest to cultivated or construction land, providing valuable insights for optimizing conservation strategies in China’s national parks.

Socio-Economic Impacts of Internal Migration Dynamics in Morogoro Municipality, Tanzania

Internal and external migrations differ significantly in their social, economic, and environmental drivers; as well as their impacts on individuals and regions. This article critically assesses the socio-economic and environmental drivers of internal migration, alongside its effects in Morogoro Municipality, Tanzania. A cross-sectional study was conducted utilizing a purposive sample of 201 internal migrants from three wards. Data was collected through a mixed-methods approach that incorporated both quantitative and qualitative techniques. Quantitative data was analysed using descriptive statistics, while qualitative data was examined thematically for deeper insights. The findings revealed that 60% of the migrants were rural-urban movers, primarily originating from Dodoma region. Employment opportunities (34%) and business prospects (31%) emerged as the primary drivers of this migration. However, the rapid population growth resulting from these movements has placed significant pressure on local services, particularly in water supply, market facilities, and waste management. Markedly, 82% of respondents identified inefficient collection of household waste as a major challenge, underscoring the strain on the waste management system. This article concludes that while internal migration contributes to economic diversification and demographic changes in Morogoro Municipality, it has also intensified demands on infrastructure and public services. Therefore, it is crucial for local authorities to prioritize investment in infrastructure, especially in water supply and waste management systems. Comprehensive urban planning is essential to balance residential and commercial development, while safeguarding environmental resources. Similarly, policymakers are urged to adopt strategic approaches to effectively manage the impacts of internal migration, and promote sustainable urban development.

Modelling wildfire activity in wildland–urban interface (WUI) areas of Sardinia, Italy

Background Wildfire frequency, magnitude and impacts in wildland–urban interface (WUI) areas are increasing in the Mediterranean Basin. Aims We investigated the role played by socio-economic, vegetation, climatic, and zootechnical drivers on WUI wildfire patterns (area burned and wildfire ignitions) in Sardinia, Italy. Methods We defined WUI as the 100-m buffer area of the anthropic layers. We created a comprehensive and multi-year dataset of explanatory variables and wildfires, and then trained a set of models and evaluated their performances in predicting WUI fires. We used the best models to assess the single variable’s importance and map wildfire patterns. Key results Random Forest and Support Vector Machine were the best performing models. In broad terms, wildfire patterns at WUI were influenced by socio-economic factors and herbaceous vegetation types. Conclusions Machine learning models can be useful tools to predict wildfire ignitions and area burned at WUI in Mediterranean areas. Implications Improved knowledge of the main drivers of wildfires at WUI in fire-prone Mediterranean areas can foster the development or optimisation of wildfire risk reduction and prevention strategies.

GIS-based analysis of anti-Asian hate speech and its socioeconomic and ideological drivers in the United States during the early COVID-19 pandemic

Anti-Asian racism and xenophobia surged on social media during the COVID-19 pandemic, fueling discrimination and violence against Asians and Asian Americans in the United States. This study aims to understand the spatial patterns of Anti-Asian hate speech (AAHS) and identify the socioeconomic and ideological factors that may influence its emergence. Using Geographic Information System (GIS) tools, including hotspot analysis, Ordinary Least Squares (OLS), and Geographically Weighted Regression (GWR), we analyzed several socioeconomic and ideological factors to understand their impact on the spatial variability of AAHS risk levels. Our models show AAHS hotspots tend to cluster in suburban areas near the metropolitan and micropolitan regions. Our OLS model identifies three significant socioeconomic factors: the poverty level is negatively correlated with the AAHS risk level, but both high education level and diversity index demonstrate positive correlations with the AAHS risk level. The GWR model further explored local spatial patterns of how these factors influence AAHS levels. We suggest that anti-Asian sentiments may be driven by dominant groups fearing the loss of their identity and socioeconomic security. Additionally, in diverse areas, pandemic-related crises can exacerbate existing intergroup tensions, with structural inequalities further deepening divisions. Policymakers can leverage these findings to develop strategies aimed at mitigating the factors contributing to hate speech and effectively combating it. Future studies can expand on our findings to develop more robust theoretical frameworks for understanding these patterns.

Measuring and validating the Ukrainian ethnic coherence instrument

ABSTRACT To assess Ukrainian ethnic coherence, scholars have employed single-item scales such as language preference and national identity. In practice, these approaches can downplay the multidimensional nature of ethnic coherence. To address this concern, I propose a multidimensional theory of Ukrainian ethnic coherence and validate its measure with a survey in government-controlled Ukraine (n = 1,004). The factor analysis validated an 18-item scale composed of four robust indicators: myths of origin and fate, symbols of territory, language as a community symbol, and traditions of collective action. I then assessed the level of shared understanding of the four indicators and their relationship to demographic characteristics. Ethnic Ukrainians demonstrated a high level of shared understanding of myths of origin and fate and language as a community symbol, followed by traditions of collective action. Nevertheless, they demonstrated low agreement about which territorial boundaries represent ethnic Ukrainians. The study also presents socio-economic drivers that influence ethnic coherence.

Hybrid performances in sport: Cybathlon spectatorship for critically imagining technologies for disability futures

Disabled bodies have been historically marginalised in sporting arenas and spectacles. Assistive technologies have been increasingly featuring in, and changing, sporting landscapes. In some ways recent shifts have made disability...

Evaluation of global fire simulations in CMIP6 Earth system models

Abstract. Fire is the primary form of terrestrial ecosystem disturbance on a global scale and an important Earth system process. Most Earth system models (ESMs) have incorporated fire modeling, with 19 of them submitting model outputs of fire-related variables to the Coupled Model Intercomparison Project Phase 6 (CMIP6). This study provides the first comprehensive evaluation of CMIP6 historical fire simulations by comparing them with multiple satellite-based products and charcoal-based historical reconstructions. Our results show that most CMIP6 models simulate the present-day global burned area and fire carbon emissions within the range of satellite-based products. They also capture the major features of observed spatial patterns and seasonal cycles, the relationship of fires with precipitation and population density, and the influence of the El Niño–Southern Oscillation (ENSO) on the interannual variability of tropical fires. Regional fire carbon emissions simulated by the CMIP6 models from 1850 to 2010 generally align with the charcoal-based reconstructions, although there are regional mismatches, such as in southern South America and eastern temperate North America prior to the 1910s and in temperate North America, eastern boreal North America, Europe, and boreal Asia since the 1980s. The CMIP6 simulations have addressed three critical issues identified in CMIP5: (1) the simulated global burned area being less than half of that of the observations, (2) the failure to reproduce the high burned area fraction observed in Africa, and (3) the weak fire seasonal variability. Furthermore, the CMIP6 models exhibit improved accuracy in capturing the observed relationship between fires and both climatic and socioeconomic drivers and better align with the historical long-term trends indicated by charcoal-based reconstructions in most regions worldwide. However, the CMIP6 models still fail to reproduce the decline in global burned area and fire carbon emissions observed over the past 2 decades, mainly attributed to an underestimation of anthropogenic fire suppression, and the spring peak in fires in the Northern Hemisphere mid-latitudes, mainly due to an underestimation of crop fires. In addition, the model underestimates the fire sensitivity to wet–dry conditions, indicating the need to improve fuel wetness estimation. Based on these findings, we present specific guidance for fire scheme development and suggest a post-processing methodology for using CMIP6 multi-model outputs to generate reliable fire projection products.

The Impact of the Integration of International Immigrants on the Cost of Labor in the Labor Market

Objectives: The study aims to analyze the impact of international immigrant integration on labor costs within the labor market. It seeks to examine migration trends, identify the primary socio-economic drivers, and evaluate migration policies and their effects on labor dynamics in various countries. Methods: A systematic approach was employed to categorize immigration types, assess migrant numbers, and evaluate their actions. Comparative analysis of hourly labor costs across countries was conducted to understand the economic impact of migration. The study also examined the influence of geopolitical conflicts, such as those between Russia-Ukraine and Israel-Palestine, on migration trends. Results: The analysis revealed a significant decrease in migration levels during 2020-2021, ranging from 3.8 to 4.8 million migrants, influenced by global economic conditions and geopolitical conflicts. Factors contributing to permanent migration include the pursuit of socio-economic opportunities and the fallout from regional military conflicts. The labor market, education, and investment were identified as critical mechanisms for immigrant assimilation. Moreover, the study highlighted the importance of aligning migrant objectives with host country goals to foster economic progress and improve living standards. Conclusion: The integration of international immigrants significantly impacts labor market dynamics, highlighting the need for effective migration policies. These policies should focus on balancing the socio-economic aspirations of migrants with the developmental goals of host countries to achieve sustainable economic progress and enhance societal cohesion.

Female and male treatable mortality: socioeconomic and public finance related factors across European countries.

About 36.5% of premature deaths in European Union countries could have been avoided through prompt and effective medical treatment. This treatable mortality is even a priority established in Sustainable Development Goal (SDG) target 3.4. Given the gap in the literature about the socioeconomic drivers of this type of mortality, as well as the increasing importance of public financial management in defining priority policies, this study aims to analyze the socioeconomic and public finance drivers associated with treatable mortality for women and men across European countries. Eurostat data is collected for 31 countries for the period 2011-2019 stratified by sex. Panel data quantile regression with fixed effects and conditional mean panel data model using feasible generalized least squares are estimated to explain treatable mortality in women and men. Key findings point to a positive association between the public finance indicator proxying health priority and the treatable mortality rate for both sexes; a difference between drivers of treatable mortality between men and women; and a different set of drivers across the different quantiles of treatable mortality. Drivers of male and female treatable mortality may differ according to the country's level of mortality rate. Government health priority seems to account for previous treatable mortality rates as a reactive measure. Policymakers aiming to reduce treatable mortality are likely to use instruments such as health expenditure, improved employment, education levels, and perhaps proactive policy-setting priorities concerning health.

A Dual Burden Dilemma: Navigating the Global Impact of Communicable and Non-Communicable Diseases and the Way Forward

The global burden of disease has shifted significantly, with communicable diseases (CDs) like HIV/AIDS, tuberculosis, and malaria increasingly intersecting with the rising prevalence of non-communicable diseases (NCDs) such as cardiovascular diseases, cancers, and diabetes. This narrative review explores the dual burden posed by CDs and NCDs, emphasizing their interconnections, societal impacts, and regional disparities. While CDs continue to challenge healthcare systems in low- and middle-income countries, NCDs dominate global mortality, driven by aging populations, urbanization, and lifestyle changes. The review highlights how infectious agents contribute to chronic diseases, such as hepatitis B leading to liver cancer or COVID-19 exacerbating diabetes and cardiovascular risks. It underscores the necessity for integrated healthcare approaches that address both disease categories to reduce health inequities. Regional and demographic disparities, such as higher disease burdens in sub-Saharan Africa and Southeast Asia, are also discussed, along with their socio-economic drivers. Global efforts, including vaccination programs, lifestyle interventions, and sustainable health policies, are evaluated for their effectiveness and challenges. The review concludes with a call for innovative, equity-focused strategies to mitigate the dual burden and strengthen global health resilience.