Measurement and calculation of carbon intensity based on ImPACT model and scenario analysis: A case of three regions of Jiangsu province

• An ImPACT model is employed to examine the carbon intensity for 2015 and 2020 of three regions of Jiangsu province.
• We make a scenario analysis of the economic development, population size, energy intensity, and energy consumption structure.
• The northern and southern regions of Jiangsu province can achieve the 40–45% reduction target for carbon intensity in 2020 in all of the scenarios.
• The energy intensity and energy consumption structure are the key factors for carbon intensity reduction.

In order to find the best approach to achieving the carbon intensity reduction targets of Jiangsu province, we used an ImPACT model to undertake a scenario analysis of the economic development, population size, energy intensity, and energy consumption structure of three regions of Jiangsu province. Based on carbon intensity reduction targets of 40% and 45%, the carbon intensity in different scenarios for 2015 and 2020 were calculated in order to find the optimum emission reduction scenarios for the three regions of Jiangsu province. The research results suggest the following. Only the northern and southern regions of Jiangsu province can achieve the 40–45% reduction target for carbon intensity in 2020 in all of the scenarios. The northern regions of Jiangsu province have a minimum carbon intensity of 0.525414 tons per ten thousand yuan in the scenarios where the economic development and population size are at a medium rate of increase and the energy intensity and energy consumption structure are at a high rate of change. The middle regions of Jiangsu province have a minimum carbon intensity of 0.566539 tons per ten thousand yuan in the scenarios where the economic development is at a high rate of increase and the energy intensity and energy consumption are at a high rate of change. The southern regions of Jiangsu province have a minimum carbon intensity of 0.613281 tons per ten thousand yuan in the scenarios where the energy intensity and energy consumption structure are at a high rate of change. On the basis of these findings, this study developed relevant policies and suggestions.