Assessing energy efficiency in the built environment: A quantile regression analysis of CO2 emissions from buildings and manufacturing sector
Year: 2025
Type: article
Abstract: Achieving sustainable development has emerged as a global emergency in response to environmental challenges, including the need to mitigate carbon dioxide (CO 2 ) emissions. The built environment, encompassing both buildings and manufacturing activities, is critical to achieving net zero, given its contribution of approximately 37% to global energy-related CO 2 emissions. While earlier research explored CO 2 emissions broadly, we lack a clear picture of how built environments respond to various environmental and economic factors, especially as economies modernize their infrastructure and cities. This research provides insights into the sustainable transformation of the built environment by analyzing the impact of energy efficiency, urbanization, industrial development, and non-renewable energy on building and manufacturing-based CO 2 emissions in newly industrialized countries during 1990–2020. To this end, the study employed the Method of Moments Quantile Regression (MMQR) that enables analysis of heterogeneous effects across the conditional distribution of emissions, extending beyond conventional mean-based estimations. The findings from the MMQR show that improvement in energy efficiency mitigates CO 2 emissions from the built environment. Specifically, a 1% improvement in energy efficiency results in a reduction in CO 2 emissions ranging from 0.913% to 0.835% across all quantiles. The “Environmental Kuznets Curve (EKC)” holds for built environment-based CO 2 emissions in sample countries. The results indicate that non-renewable energy consumption and industrialization significantly increase emissions from the built environment. Policy efforts should prioritize renewable energy sources, energy-efficient designs, and a balanced growth path to steer the building sector toward a lower carbon footprint for long-term environmental sustainability .
Source: Energy and Buildings
Language: English
Subfield: Economics and Econometrics
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $4,260
OpenAlex ID: W4409259693
