Agroforestry and Sustainable Development: A Novel Perspective of Environmental Resilience via Agroforestry and Globalization

ABSTRACT The main aim for sustainable development is to guarantee the provision of resources to the existing generation without compromising the needs of future generations. Agroforestry, the integrated management of trees and crops on the same land unit, is considered the important source of economic prosperity. The agricultural sector is a key contributor to global warming. Due to the heavy use of traditional production techniques, the agriculture sector is linked with environmental sustainability. However, the agriculture sector significantly contributes to economic growth by increasing production and generating employment. Since economic growth is a fundamental component of sustainable development, the agriculture sector's contribution to economic growth is essential for achieving broader sustainability goals. In addition to the agriculture sector, forests are generally considered a source of income generation and environmental sustainability. Forests play an important role in mitigating climate change through carbon sequestration. This study investigates the role of agriculture, forestry, and fishing value added, on sustainable development in the case of G7 countries. Moreover, we include globalization, green innovation, financial development, and environmental related taxes as additional determinants of sustainable development. Employing the quantile regression approach, the study found that agriculture, forestry, and fishing value added, globalization, green innovation, financial development, and environmental related taxes are important factors affecting sustainable development in G7 countries. The results show that agriculture, forestry, and fishing value added, globalization, green innovation, financial development, and environmental related taxes are important variables affecting sustainable development, particularly from the medium to long run. For robustness check, this study uses Driscoll‐Kraay and panel corrected standard errors approaches, which correct heteroscedasticity in the model. The results obtained are aligned with the estimates obtained from the QR approach.