Prediction of Shanghai Electric Power Carbon Emissions Based on Improved STIRPAT Model

Haibing Wang (), Bowen Li and Muhammad Qasim Khan
Additional contact information
Haibing Wang: Department of Electrical Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China
Bowen Li: Department of Electrical Engineering, University of Shanghai for Science and Technology, 516 Jungong Road, Yangpu District, Shanghai 200093, China
Muhammad Qasim Khan: Key Laboratory of Control of Power Transmission and Conversion (SJTU), Ministry of Education, 800 Dongchuan Road, Minhang District, Shanghai 200240, China

Sustainability, 2022, vol. 14, issue 20, 1-15

Abstract: Energy is the bridge connecting the economy and the environment and electric energy is an important guarantee for social production. In order to respond to the national dual-carbon goals, a new power system is being constructed. Effective carbon emission forecasts of power energy are essential to achieve a significant guarantee for low carbon and clean production of electric power energy. We analyzed the influencing factors of carbon emissions, such as population, per capita gross domestic product (GDP), urbanization rate, industrial structure, energy consumption, energy structure, regional electrification rate, and degree of opening to the outside world. The original Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model was improved, and the above influencing factors were incorporated into the model for modeling analysis. The ridge regression algorithm was adopted to analyze the biased estimation of historical data. The carbon emission prediction model of Shanghai electric power and energy based on elastic relationship was established. According to the “14th Five-Year” development plan for the Shanghai area, we set up the impact factor forecast under different scenarios to substitute into the forecast models. The new model can effectively assess the carbon emissions of the power sector in Shanghai in the future.

Keywords: electric energy; carbon forecast; STIRPAT model; ridge regression; scenario analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/14/20/13068/pdf (application/pdf)
https://www.mdpi.com/2071-1050/14/20/13068/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:14:y:2022:i:20:p:13068-:d:940132

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().