While numerous studies have examined the individual relationships between land use, electricity access, economic growth, and population dynamics, few have integrated these factors to understand their combined impact across regions. This study aims to fill these gaps by proposing a central hypothesis and adopting a multidisciplinary approach that considers how regional differences shape development outcomes. By building on studies such as Lambin et al. (2000) and Wang et al. (2019) that emphasize decision-making and the socioeconomic impacts of land use transitions, this study aims to inform sustainable development strategies that consider both local needs and global linkages. In addition, integrating Malthusian theory with modern insights on human capital and technological advances provides a more nuanced perspective on population dynamics and their implications for land use and resource management. This approach will help to develop more accurate models for understanding the complex relationship between population growth, land use, and economic development.

This study used secondary data from reputable global databases to ensure the accuracy and credibility of the analysis. The primary data source was the World Bank Development Indicators, which provides standardized data on electricity access, GDP growth, population growth, agricultural land, and arable land. The key variables examined include: agricultural land, arable land, electricity access, GDP growth and population growth.

To explore the complex relationships between the variables, the study employed a variety of statistical methods to ensure robust analysis that could capture both global and regional nuances. Descriptive statistics were used to summarize and compare key variables, such as access to electricity, agricultural land use, and GDP growth across regions. Given the regional differences in baseline conditions, including access to electricity and demographic trends, the descriptive analysis highlights these differences to contextualize the relationships between variables. Correlational analysis helped identify relationships between variables, providing insight into their interrelationships. Multiple linear regression models were developed to examine predictors of economic growth. Moderation analysis was used to examine the interaction between the variables and its effect on outcomes. Finally, analysis of variance (ANOVA) was used to compare the means of key variables across regions and to identify statistically significant differences. This combination of techniques provided a comprehensive understanding of the factors shaping regional development dynamics.

The regression model presented in Table 3 explains 13.7% of the variation in GDP growth, as indicated by R²=0.137. After adjusting for the number of predictors, the adjusted R² is slightly lower at 0.120. This suggests that while the model has some explanatory power, it leaves a significant amount of variability unexplained. The model is statistically significant overall (F (4,202) =8.05, p<0.001), confirming that the included predictors collectively contribute to explaining differences in GDP growth. However, the modest R² highlights the potential influence of additional unexamined variables on GDP growth.

The coefficients presented in Table 4 provide detailed insights into the direction and magnitude of the impact of each predictor on GDP growth. The intercept (B=-1.459, p=0.440) is not statistically significant, indicating that GDP growth is not inherently negative when all predictors are zero. Agricultural land (% of land area) is a significant positive predictor (B=0.096, p=0.004), indicating that an increase in agricultural land is strongly correlated with higher economic growth. In contrast, arable land (% of land area) has no significant effect on GDP growth (B=0.0098, p=0.663). Similarly, access to electricity (% of population) has a minimal impact (B=0.0038, p=0.748), further supporting its lack of significant direct effect on economic growth in this model. Population growth (annual %) shows a moderate positive effect (B=0.540, p=0.085), but its lack of statistical significance indicates that the relationship is not strong enough to draw definitive conclusions.

The moderation analysis examines how the relationship between population growth and GDP growth is affected by the level of access to electricity. The results show three main findings:

Population growth (annual %): The main effect of population growth on GDP growth is negative (estimate=-1.4723), with a large Z-value of -9.17 and a p-value of less than 0.001. This indicates a strong, statistically significant negative relationship between population growth and GDP growth, suggesting that higher population growth may be associated with lower economic growth under typical conditions.

Access to electricity (% of population): The effect of access to electricity on GDP growth is also negative (estimate=-0.0960) and highly significant (Z=-15.37, p<0.001). This suggests that, on average, greater access to electricity is correlated with lower GDP growth in this analysis, although this result may warrant further investigation given the broader context or indirect pathways through which access to electricity affects economic performance.

Interaction between population growth and access to electricity: The interaction term (estimate=0.0612) is positive and highly significant (Z=7.73,p<0.001), indicating that the negative effect of population growth on GDP growth becomes weaker (or even positive) as access to electricity increases. This suggests that in regions with higher access to electricity, the negative impact of population growth on GDP growth is mitigated or less pronounced.

The one-way ANOVA presented in Table 6 tests the differences between regions for several indicators, including GDP growth, access to electricity, population growth, arable land, and agricultural land. The Welch’s and Fisher’s F-tests indicate that there are significant differences between the regions for all variables:

GDP growth (annual %): Both Welch’s (F=5.17, p<0.001) and Fisher’s (F=5.89, p<0.001) tests indicate significant regional differences in GDP growth, suggesting that economic growth patterns differ significantly across regions.

Access to electricity (% of population): Welch’s (F=539.92, p<0.001) and Fisher’s (F=443.42, p<0.001) tests show highly significant differences in access to electricity. This indicates significant regional differences in access to electricity, reflecting differences in infrastructure and development.

Population growth (annual %): Significant regional differences in population growth are confirmed by both Welch’s (F=1445.17, p<0.001) and Fisher’s (F=663.49, p<0.001) tests. This suggests that population growth rates differ significantly across regions, likely due to factors such as fertility rates, migration patterns, and socio-economic conditions.

Arable land (% of land area): Both Welch’s (F=21415.28, p<0.001) and Fisher’s (F=9333.11, p<0.001) tests indicate highly significant differences in the proportion of arable land, which varies by region due to differences in land use, geography, and agricultural practices.

Agricultural Land (% of Land Area): Differences in agricultural land are similarly significant, with Welch’s (F=24416.05, p<0.001) and Fisher’s (F=1076.16, p<0.001) tests indicating notable regional differences in the amount of land devoted to agriculture.

The descriptive analysis reveals significant regional differences in agricultural and cropland use, highlighting the influence of geography, economic structure and regional levels of development on land use patterns. For example, South Asia’s high agricultural land use (56.7%) is driven by the region’s predominantly agrarian economies, where agriculture remains a critical sector for employment and food security. This finding is consistent with previous research highlighting the role of agricultural land use in developing economies (Apergis & Ozturk, 2015). In contrast, Europe and Central Asia (29.4%) and Central Europe and the Baltics (47.9%) have lower agricultural land use, reflecting the advanced industrialization and urbanization in the region, which has reduced the share of land devoted to agriculture.

The minimal use of arable land in the Middle East and North Africa (4.75 percent) illustrates how regional climatic constraints, such as arid and semi-arid environments, shape land use practices. The region faces significant challenges related to water scarcity and desertification, which limit the availability of arable land. These regional differences underscore the need for context-specific agricultural policies that take into account environmental factors, economic realities and the different levels of development in the regions.

Correlation analysis supports these observations, showing a strong relationship between agricultural land and cropland (r = 0.704, p < 0.001). However, the weak negative correlation between agricultural land and access to electricity (r = -0.165, p = 0.018) suggests that rural agricultural economies face significant infrastructure challenges. This is particularly evident in regions such as sub-Saharan Africa, where rural electrification rates remain critically low despite high levels of agricultural activity. This highlights the importance of addressing infrastructure deficits in these regions to promote rural development and increase agricultural productivity.

The differences in access to electricity between regions are striking; they reflect significant differences in infrastructure. Regions such as Europe and Central Asia and Central Europe and the Baltic States have almost universal access to electricity, reflecting their well-developed energy infrastructure and high levels of economic development. In contrast, regions such as Eastern and Southern Africa (31.8%) and sub-Saharan Africa (36.9%) continue to face significant electrification challenges. These regions are characterized by underdeveloped energy infrastructure, limited investment in renewable energy, and significant urban-rural disparities in access to electricity.

Moreover, the high standard deviations in regions such as South Asia (13.6%) and sub-Saharan Africa (8.10%) underscore the uneven distribution of electricity within these regions. In South Asia, urban centers have made significant progress in electrification, while rural areas, particularly in less developed parts of the region, continue to lag behind. These disparities highlight the need for targeted interventions to bridge the urban-rural divide in electricity access.

Correlation analysis shows a weak negative correlation between access to electricity and GDP growth (r = -0.189, p = 0.006), suggesting that more developed regions with near-universal access to electricity experience slower economic growth. This is consistent with the characteristics of mature economies, where high levels of infrastructure development are associated with stabilized growth rates. Moreover, the strong negative correlation between electricity access and population growth (r = -0.834, p < 0.001) is consistent with demographic transitions in developed regions, where improvements in infrastructure are associated with lower fertility rates and stabilized population growth (Lee, 2003). These results highlight the critical role of electricity infrastructure in driving both economic development and demographic change.

There is considerable variation in patterns of economic growth across regions, with South Asia (5.76%) and East Asia and the Pacific (4.83%) showing high GDP growth indicative of rapid industrialisation and market expansion. These regions benefit from dynamic industries, favourable demographics and increasing integration with global markets. In contrast, Europe and Central Asia experienced the slowest growth (1.89%), reflecting the characteristics of mature economies that have reached their growth potential. This region is facing challenges such as ageing populations, low productivity growth, and economic stagnation in some areas.

Interestingly, the weak positive correlation between GDP growth and population growth (r = 0.165, p = 0.017) suggests that while economic expansion may encourage larger populations, other factors, such as fertility rates and access to healthcare, are likely to play a more important role in influencing demographic trends. For example, in sub-Saharan Africa, high population growth rates are driven by socio-cultural factors and improved healthcare rather than economic performance alone.

The moderation analysis provides valuable insights into the dynamics between population growth, electricity access and GDP growth. The analysis shows that population growth negatively affects GDP growth in regions with low access to electricity. This is particularly evident in regions such as sub-Saharan Africa, where rapid population growth coupled with low electrification rates exacerbates economic challenges. However, this negative impact is mitigated in regions with better access to electricity. The interaction term (r = 0.0612, p < 0.001) suggests that improving electricity infrastructure can mitigate the negative effects of rapid population growth, making it a critical component of sustainable development in fast-growing regions. For example, regions such as East Asia and the Pacific have been able to sustain high economic growth despite population pressures by investing heavily in energy infrastructure.

One-way ANOVA tests confirm significant differences across regions for all variables considered, highlighting the regional disparities that characterize global development. These differences are driven by different levels of economic development, infrastructure, and demographic structures across regions. For example, regions with high population growth rates, such as sub-Saharan Africa and West and Central Africa, require policies that prioritize health care, education, and infrastructure development to manage demographic transitions and ensure sustainable economic growth. In contrast, regions such as Europe and Central Asia need policies that address population ageing and economic stagnation.

The results of this study highlight the complex interactions between land use, electricity access, economic growth, and demographic trends in different regions. These interdependencies underscore the need for region-specific, integrated policies that address infrastructure gaps, environmental constraints, and socioeconomic disparities, while taking into account the unique starting conditions and demographic characteristics of each region.

While this study provides valuable insights into the relationships between agricultural land use, electricity access, economic growth, and demographic trends across macro-regions, several limitations should be acknowledged. A key limitation is the reliance on secondary data from the World Bank and other international databases, which, while comprehensive, may not fully capture the complexity of local economic conditions, policy interventions, or emerging trends in infrastructure development. The accuracy and reliability of these data depend on the reporting standards and data collection methods used by individual countries, which can vary significantly across regions.

In addition, this study primarily analyzes trends at the regional level, which may obscure important intra-regional or sub-national variations. In macro-regions with high internal diversity, such as sub-Saharan Africa or Europe and Central Asia, aggregated statistics may mask significant disparities between countries. Future research could benefit from subnational analysis or case studies to provide a more detailed understanding of the dynamics at play.

Future research should explore the dynamic interplay between technological progress, policy interventions and regional development outcomes. The role of innovations in renewable energy and sustainable agricultural practices should be studied in depth to understand their potential for scaling up in different regional contexts. It is also important to include a broader range of factors that influence development, such as climate change, quality of governance, and global trade, to provide a more comprehensive analysis. Finally, comparative case studies of successful development models could help identify best practices and strategies for overcoming common barriers in energy access, agricultural development and demographic management.