Abstract：Urban residential energy consumption and CO2 emissions have a major impact on regional carbon reduction policy.Due to lack of data and limitation of methods, spatial characteristics of urban residential CO2 emissions and its influencing factors at county scale have rarely been discussed.Therefore, taking the Central Plains Economic Region as a case, this paper used the DMSP-OLS nighttime light imageries corrected by enhanced saturation correction model to estimate the spatial distribution of carbon emissions at the 1 km resolution and analyze its influencing factors at the county scale with geographical weighted regression model.The rationality of our method was confirmed in the process of estimating carbon emissions.The linear regression model between the estimated CO2 emissions and the statistical CO2 emissions of urban residents with R2 of 0.837 1 demonstrated that the inversion model based on the nighttime light imageries has strong feasibility and suitability.Based on the carbon emissions of urban residents at 1 km resolution, we can calculate carbon emissions across administrative boundaries.In terms of spatial characteristics, CO2 emissions in the northwest Central Plains Economic Region were significantly higher than those in the southeast.Moreover, Zhengzhou District held the first place of CO2 emissions with total amount of 2.47 ×106 t, accounting for 6.58% of total CO2 emissions.However, Xingtai, Huixian and Xiangyuan were the regions with high CO2 emissions per capita, and these regions with higher carbon emissions per capita should be paid more attention.With respect to influencing factors, per capita GDP, carbon intensity, proportion of secondary industry and HDD (Heating Degree Days) all have the positive effects on urban residential carbon emissions at the county scale.However, urbanization rate presented a negative effect.Furthermore, it's important to note that CDD (Cooling Degree Days) has positive impact on urban residential carbon emissions in some cities while has negative impact in other cities.However, the maximum coefficient of its negative impact was 0.046 6, which could be ignored compared with the coefficient of positive impact.Therefore, CDD would be regarded as a positive influencing factor as a whole.Overall, the analysis of influencing factors in this paper provides an important theory basis for policy-makers to carry out more feasible policy on regional carbon emissions in the Central Plains Economic Region.
Keyword：urban resident; CO2 emissions; nighttime light; Central Plains Economic Region;
【source】Journal of Natural Resources 2017年12期
