The Forecasting Research on the Scenarios of Energy Consumption Carbon Peaking in the Guangdong-Hong Kong-Macao Greater Bay Area
This study employs a combined methodology integrating the IPCC energy consumption approach and night-time light data inversion to estimate carbon emissions stemming from energy usage across cities within the Guangdong-Hong Kong-Macao Greater Bay Area spanning the period from 2005 to 2021.Utilizing an extended STIRPAT forecasting model,scenario assumption method,and Monte Carlo dynamic simulation,this study simu-lates and analyzes potential carbon peaking pathways for the Greater Bay Area and proposes targeted policy rec-ommendations.Findings reveal a fluctuating upward trajectory in carbon emissions within the Greater Bay Area during the specified timeframe.Hong Kong achieved carbon peaking in 2014,while Macao exhibited a relatively minor contribution to total carbon emissions.Notably,the nine cities within the Pearl River Delta demonstrated a declining trend post-2011,followed by fluctuating growth post-2016.Dynamic simulations under various scenari-os—baseline,low-carbon,extremely low-carbon,industrial transformation,and all-around low-growth—suggest that the Greater Bay Area could attain its 2030 carbon peak target as scheduled,or even surpass it ahead of sched-ule.The adoption of more proactive carbon reduction and industrial transformation policies could propel an earli-er realization of the carbon peak.
Guangdong-Hong Kong-Macao Greater Bay Areacarbon emissions of energy consumptioncarbon peakgreen and low-carbonscenario prediction
万方数据
维普
