The transport infrastructure plays an imperative role in a country's progress. At the same time, it causes environmental degradation due to extensive use of fossil fuels. The transport system of Pakistan is largely dependent on nonrenewable energy sources (oil, coal, and gas), which are hazardous to environmental quality. This research uses an autoregressive distributive lag model (ARDL) to examine the impact of oil prices, energy intensity of road transport, economic growth, and population density on carbon dioxide (CO2) emissions of Pakistan's transport sector during the 1971-2014 period. The ARDL bounding test examines the cointegration and long-run relationships among the variables, and the directions of causal relationships are found through the Granger causality vector error correction model (VECM). The long-run results indicate that increases in oil prices and economic growth help to reduce the transport sector's CO2 emissions, while rising energy intensity, population concentration, and road infrastructure increase them, with population playing a dominant role. The findings of this study can help authorities in Pakistan to develop suitable energy policies for the transport sector. Among other recommendations, the study recommends investment in renewable energy projects and energy-efficient transport systems (e.g., light train, rapid transport system, and electric busses) and environmental taxes (subsidies) on the vehicles that use fossil fuels (renewable energy).
Keywords: CO2 emissions from transport; Pakistan; Road transport energy intensity; STIRPAT.