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Many challenges are faced in the process of urban sustainable development, including the continuous growth in energy demand and rapid increase in CO2 and air pollutant emissions. This study focuses on the costs of measures to address these issues and establishes a multi-objective comprehensive assessment model for energy saving, CO2, and pollutant emission (MCEE). Taking Guangzhou as an example, the sustainable development measures are divided into three categories, energy-saving, demand-optimization, and environmental-protection. Five scenarios are set to quantitatively evaluate the costs when these measures are implemented alone or coordinately for the period 2015–2035. Conclusions are as follows: (1) Measures of energy-saving and demand-optimization have the best synergistic effect on energy saving and emission reduction. The synergistic benefits include an 80% and 84% increase in energy savings and CO2 reductions, respectively, and more than 50% increase in pollutant reductions. (2) Measures of demand-optimization and energy-saving have the best synergistic effect on cost saving, which reduces the unit technical improvement costs of energy saving and CO2 reduction by 49.5% and 54.9%, respectively, and the unit end-of-pipe costs of four pollutants by 59.15%, 54.43%, 61.15%, and 51.96, respectively. (3) Environmental-protection measures have remarkable synergistic effects in reducing the cost of health loss and labor loss. At the price of a 5% increase in technical improvement cost and 9% in end-of-pipe treatment cost, health loss, labor loss, and total social cost will be reduced by 18%, 19%, and 3%, respectively. The above conclusions provide support for cities of the same type to coordinate various measures, reduce resistance and barriers to their implementation, compensate for the market deficiency of high costs of some measures, and achieve the goal of sustainable development.

energies

Assessment on the Cost Synergies and Impacts among Measures on Energy Conservation, Decarbonization, and Air Pollutant Reductions Using an MCEE Model: A Case of Guangzhou, China