Abstract
Greenhouse gases (GHGs) are one of the leading causes of global warming. Therefore, accuracy estimates for greenhouse gases (GHGs) emissions are a key element in defining the best strategies for reducing GHG emissions from various source sectors of the economy. In the present study, an initial attempt has been made to estimate and forecast the GHG emissions in Pakistan from five major sectors, such as energy, industrial, agriculture, waste, and land-use change and forestry. The data were taken from the official website of the Pakistan climate database from 1990 to 2016. We employed advanced mathematical modeling, namely a non-homogenous discrete grey model (NDGM), to predict sector-wise GHGs emissions. Moreover, the present study is a milestone in the GHGs growth analysis by utilizing the synthetic relative growth rate (SRGR) and synthetic doubling time model (SDTM). The results reveal that the industry and land-use change and forestry contribute more in terms of increasing GHGs emissions till 2024, whereas agriculture and waste required comparatively less time to reduce GHGs emissions double in number among five sectors. All five sectors show an increasing trend in forecasting GHGs emissions between 1990 and 2016. However, the results indicate that land-use change and forestry and industrial sectors are more likely to be a reason for the increase in GHGs emissions in the future, followed by the agriculture, energy, and waste sectors. The land-use change and forestry was found prone to increase emission in the future, and the doubling time (\({D}_{\mathrm{t}}\)) suggests less time expected to reduce GHGs. Finally, the study has suggested some policies for the policymakers, government, and decision-makers to reduce GHGs emissions and achieve sustainable development.
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Rehman, E., Ikram, M., Rehman, S. et al. Growing green? Sectoral-based prediction of GHG emission in Pakistan: a novel NDGM and doubling time model approach. Environ Dev Sustain 23, 12169–12191 (2021). https://doi.org/10.1007/s10668-020-01163-5
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DOI: https://doi.org/10.1007/s10668-020-01163-5