摘要:查明碳排放时空演变成因、经济发展脱钩情形及其影响因素，对协调区域经济发展同时更好进行碳减排具有指导意义。以欠发达江西省为例，基于2002-2016年规模以上工业主要能源消费数据，用IPCC碳核算方法、Tapio脱钩模型及对数平均迪氏指数分解法（Logarithmic Mean Divisia Index，LMDI）对该域能耗碳排放时空演变特征、与经济发展脱钩关系及其驱动因素进行了分析。结果表明：（1）江西省碳排放量前期快速增长、中后期增长减缓，由2002年的6248.57×10⁴ t增加到2016年的18680.47×10⁴ t，增长率高达198.96%。碳排放强度处于前中期快速下降，后期缓慢下降的趋势，由5.604 t/万元降为0.552 t/万元。碳排放量和碳排放强度大体呈现着西北高东南低的空间分布特征。（2）2002-2009年和2009-2016年江西省经济增长与碳排放总体上呈现弱脱钩，脱钩弹性分别为0.177和0.105。2002-2009年江西省南部和北部地区脱钩情况不够理想；2009-2016年江西省除上饶市以外的东北部地区脱钩状况较差。（3）2002-2009年，能源结构对江西省碳排放脱钩有微弱的抑制作用，2009-2016年转变为微弱的促进作用，其对各地市碳排放脱钩的驱动方向有所不同。能源强度对碳排放脱钩起主导性作用，其在两个阶段的脱钩弹性分别为-0.329和-0.481。经济水平对碳排放脱钩有主要的抑制作用，在两个阶段的脱钩弹性分别为0.377和0.475。人口对碳排放脱钩具有较小的抑制作用。因此，江西省碳减排的重点在于改善能源结构与提高能效，推动新余、九江和萍乡等城市的传统工业转型升级，促进江西西北地区与东南地区城市的绿色协同发展。



Abstract:It is significant for coordinating regional economic development and carbon emissions' reduction to find the causes of spatio-temporal evolution of carbon emissions, the decoupling status of economic development and corresponding influencing factors. Thus, taking underdeveloped Jiangxi Province as an example, we analyzed the spatio-temporal evolution characteristics of carbon emissions, the decoupling status from economic development and corresponding influencing factors in the region, by using the IPCC carbon accounting method, Tapio decoupling model and Logarithmic Mean Divisia Index (LMDI), based on the industrial energy consumption data from 2002 to 2016. The results show that: (1) carbon emissions in Jiangxi Province increased rapidly in the early stage and slowed in the middle and late stages. Overall they increased from 6248.57×10⁴ t in 2002 to 18680.47×10⁴ t in 2016, with a growth rate of 198.96%. Carbon emission intensity showed a trend of rapid decline in the early and middle periods and slow decline in the later period, from 5.604 t per 10,000 yuan to 0.552 t per 10,000 yuan. The spatial distribution of carbon emissions and carbon emissions' intensity was generally high in the northwest and low in the southeast. (2) From 2002 to 2009 and from 2009 to 2016, decoupling was weak between economic growth and carbon emissions in Jiangxi, with the decoupling elasticity of 0.177 and 0.105, respectively. From 2002 to 2009, all cities presented a weak decoupling state; the decoupling elasticity in Ganzhou and Shangrao was relatively large, 0.504 and 0.440, respectively. The decoupling state of the southern and northern regions in Jiangxi was not ideal. From 2009 to 2016, the decoupling state in Pingxiang and Shangrao transformed to strong decoupling; their decoupling state was most ideal. The decoupling state transformed from weak decoupling to expansive negative decoupling in Fuzhou. Decoupling elasticity in Jingdezhen increased from 0.179 previously to 0.741. The decoupling state was poor in the northeastern region of Jiangxi except Shangrao. (3) From 2002 to 2009, the energy structure had a weak inhibiting effect on carbon emission decoupling in Jiangxi, which was, inversely, transformed into a tiny promoting effect from 2009 to 2016; the driving direction was different in different cities. Energy intensity played a leading role in carbon emission decoupling. The decoupling elasticity of the two stages was -0.329 and -0.481, respectively. Except for Fuzhou from 2009 to 2016, energy intensity played a major role in promoting carbon emission decoupling in cities. Economic level had a major inhibiting effect on carbon emission decoupling in Jiangxi; decoupling elasticity in the two stages was 0.377 and 0.475, respectively, which had a major inhibiting effect on carbon emission decoupling in various cities. Population level had a small inhibiting effect on carbon emission decoupling. Therefore, the focus of carbon emission reduction in Jiangxi is to improve energy structure and energy efficiency, promote the transformation and upgrading of traditional industries in Xinyu, Jiujiang, Pingxiang and other cities, and promote coordinated green development between the northwest and southeast regions of the province.





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