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主讲人 |
Lina Meng |
简介 |
<p><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">The role of urban carbon dioxide (CO</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; vertical-align: -1.5px;">2</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">) emissions has attracted city authorities’ attention. Several entities face challenges when developing inventory method for local communities, due to limited data. This study proposes a top-down method to estimate CO</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; vertical-align: -1.5px;">2 </span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">emissions at an urban scale, using nighttime light imagery and statistical energy data. We find that nighttime light imagery is appropriate in CO</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; vertical-align: -1.5px;">2 </span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">estimations at an urban scale. The proposed method is particularly significant for the developing countries, of which CO</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; vertical-align: -1.5px;">2 </span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">emissions increase rapidly but lack in energy data at an urban scale. It also contains some limitations due to the inherent shortcomings of the data sources and methodological errors. It has very limited value when applying in urban areas with rare population. A case study is implemented in urban China. The results show that the share of urban emissions increases over the period of 1985-2010. Meanwhile, per capita CO</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; vertical-align: -1.5px;">2 </span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">emissions in China continuously grow, the values of which are much higher than the national averages. In a spatiotemporal perspective, per capita CO</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; vertical-align: -1.5px;">2</span><span style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px;">emissions in eastern coastal China are lower than that in inland China.</span></p>
<p style="color: rgb(34, 34, 34); font-family: arial, sans-serif; font-size: 14px; margin: 0px 0px 12px;">By using index decomposition analysis, we explore the factors contributing to the carbon dioxide (CO2) emissions at ten Chinese metropolitan areas from 1985 to 2010. The booming economy and expanding urban areas are the major drivers to the increasing CO<span style="font-size: 11px;">2 </span>emissions in Chinese metropolitan areas over the examined period. The significant improvement in energy intensity is the primarily factor to decline the CO<span style="font-size: 11px;">2</span>emissions, the declined trend of which, however suspends or reverse since 2000. The decoupling effect of adjustments in economic structure only occurred in three megalopolises, namely Yangtze River Delta (YRD), Beijing-Tianjin-Heibei Megalopolis (BTJ), and Pearl River Delta (PRD). In comparison, the impacts of urban density and carbon intensity are relatively marginal. The further disaggregated decomposition analysis in the industry sector shows that energy intensity improvements were widely achieved in 36 sub-industries in PRD. As economic growth and urbanization continue, reductions in energy intensity and clean energy therefore deserve much more policy attentions due to their crucial roles in carbon reduction as well as satisfying the energy demand. </p> |
