Impact of Agriculture, Industry, Food Production, Renewable Energy Consumption, Urbanization, and Consumer Price Index on Ecological Footprint: Evidence from Asian Economies

Jean Baptiste Bernard Pea-Assounga^1*PRINCE DORIAN RIVEL BAMBI^2*Dalia Ayme Opoko Apendi¹

• ¹Jiangsu University, Zhenjiang, China
• ²Universite Marien Ngouabi, Brazzaville, Republic of Congo

This study investigates how industry, agriculture, food production, renewable energy consumption, consumer price index, and urbanization influence the ecological footprint across nine Asian countries from 1995 to 2020. Utilizing panel quantile regression and the system GMM as robustness checks, the analysis draws on data sourced from the world development indicators and the global footprint network. The findings from the panel quantile regression reveal that industry has a significant positive association with the ecological footprint across all quantile levels, with the effect becoming particularly stronger beyond the 20th and 30th quantiles. Food production shows a heterogeneous impact; it contributes to reducing ecological footprint at the lower quantiles (10th, 20th, and 30th). However, from the 40th to 90th quantiles, food production significantly increases ecological footprint, indicating that in high-impact scenarios, agricultural intensification and resource demand worsen environmental pressure. In contrast, renewable energy consumption demonstrates a statistically significant negative effect on ecological footprint, particularly evident at the 40th quantile and even more substantial at the 90th quantile. The effect of the consumer price index varies across quantiles, with positive impacts in lower quantiles and its negative effect at higher quantiles. Moreover, agriculture consistently emerges as a significant contributor to ecological footprint across all quantile levels. Finally, urbanization exhibits a dual Formatted: Not Highlight Formatted: Not Highlight and quantile-dependent effect, notably significant in the 20th and 30th quantiles. Moreover, the study incorporates the system GMM as a robustness check, which accounts for potential endogeneity, unobserved heterogeneity, and dynamic relationships among the variables, thereby ensuring more reliable and consistent estimates. Based on these findings, practical policy implications have been proposed along with the study's limitations and directions for future research.