This novel research looked into the role of public-private partnership investment in energy in affecting Pakistan’s long-term environmental sustainability. Employing time series data from 1992 to 2018 and utilizing the autoregressive distributive lag model (ARDL) model, we found a long-term equilibrium association of ecological footprint with public-private partnership investment in energy, technological innovation, economic growth, and trade openness. Our outcomes showed a significant positive association between public-private partnership investment in energy and ecological footprint in the long-run and the short-run, specifying that the increase in public-private partnership investment in energy affects the environmental sustainability of Pakistan. Similarly, our study confirmed that technological innovation, economic growth, and trade openness increase the ecological footprint in Pakistan. It demonstrates that these factors are unfavorable to the sustainable environment in Pakistan. Furthermore, robustness check findings are analogous to the results of ARDL estimates, utilizing dynamic ordinary least squares and fully modified ordinary least squares. On the basis of the research conclusions, a multi-pronged sustainable development goal (SDG) model was proposed that addresses SDG 8 and SDG 13 while incorporating SDG 17 as a medium.

This paper adds to the existing body of knowledge by incorporating the role of fiscal decentralization (FD) in influencing CO₂ emissions. Therefore, this study looked at the effect of FD on CO₂ emissions in the presence of nonrenewable energy consumption (NRE), renewable energy consumption (REN), gross domestic product (GDP), and trade openness (TOP) for the period 1994–2018 in Japan. Thus, the current work intends to fill this knowledge gap by employing econometric techniques such as Bayer and Hanck cointegration, dynamic ordinary least squares (DOLS), fully modified ordinary least squares (FMOLS), and canonical cointegration regression (CCR). Additionally, the frequency domain causality analysis is used in the investigation to determine the causal impact of FD, NRE, REN, GDP, and TOP on CO2 emissions. The novelty of the frequency-domain approach is that it can differentiate between nonlinearity and causality levels and show causality among parameters with different frequencies. The DOLS, FMOLS, and CCR results reveal that NRE, GDP, and TOP augment CO₂ emissions in Japan, whereas FD and REN increase the quality of the atmosphere. Furthermore, the frequency causality test results show that FD, REN, GDP, and TOP have implications for CO₂ emissions in the long run, while NRE raises CO₂ emissions in the medium run. As a policy direction, the current study suggests expanding renewable energy consumption in Japan by emphasizing more on Sustainable Development Goals (7, 8, and 13).