Study On Peak Energy Consumption And Carbon Emission Of Urban Residential Buildings In Chongqing

In recent years,carbon peaks in the building sector have received widespread attention.There are many studies on carbon peaks in the building sector,but the research object is not deep enough.The stock of buildings and the difference in energy consumption of buildings built in different years,and the overall development route of energy-saving renovation are not clear.The urban population of Chongqing exceeds 20million,and there is a large stock of urban residential buildings.It is with important guiding significance for the realization of the national building sector’s 2030 carbon peak target to carry out research on the energy-saving reduction emission path of Chongqing’s urban residential buildings.This paper aims to calculate the stock of buildings and current energy consumption of buildings built in different years,study the energy demand and carbon emission peaks of urban residential buildings in Chongqing,and analyze the feasibility,specific measures and costs of achieving different energy-saving goals.Based on theoretical research and empirical research ideas,the combined method including energy economics,econometrics and other discipline knowledge,comprehensively using statistical analysis methods,scenario analysis methods and other methods,is used to carry out the following research:Firstly,based on the“De Moivre-Laplace Limit Theorem”,a statistical model of building demolition rate is theoretically derived,and then the theoretical model is revised according to actual influencing factors,combined with the principle of the building stock rotation model,and the existing building stock information analysis model based on the statistical yearbook data is constructed,which can give the yearly stock and proportion of the buildings built in different years.Secondly,based on the principle of the STIRPAT（Stochastic Impacts by Regression on Population,Affluence,and Technology Model）model,the energy consumption intensity calculation matrix is constructed,taking into account factors such as the level of economic development,changes in energy structure,the aging of personnel,and the level of education,etc.,a time series analysis model of building energy consumption intensity based on energy balance sheet data is established.The data source of this model is authoritative and the calculation is simple.It can analyze the yearly energy consumption intensity of the buildings built in different years.Combined with the scenario analysis method,building energy consumption can also be predicted.Finally,based on the building stock analysis model and the building energy time series analysis model,combined with the development of main social and economic indicators,the energy consumption and carbon emissions of urban residential buildings in Chongqing are analyzed.The results show that primary energy demand reached the peak of 16.61 million tons of standard coal in 2054,and CO₂ emissions reached the peak of 19.596 million tons in 2044.Through the energy-saving transformation of the envelope structure,the replacement of low-efficiency energy equipment,the utilization of distributed energy in the building field,and the cleaning of the power structure,the energy-saving goal of 40%can be achieved and the primary energy consumption can be reduced to 10 million.Within one ton of standard coal.Under the energy-saving 40%scenario,it is possible to achieve the goal of peaking carbon emissions of urban residential buildings in 2030 without changing the power exchange structure with neighboring provinces,while under the 20%and 15%energy-saving scenarios,a total of 0.177 and 0.443 billion CNY respectively will be used to purchase carbon emission allowances from 2030 to 2060.This study focuses on both the macro-level strategy analysis and the micro-level practice test.It can put forward feasible strategy recommendations for the macro-policy-level emission reduction policy formulation and the micro-level practice focus,which is conducive to the early realization of emission reduction targets.