【摘要】：低碳經(jīng)濟(jì)成為應(yīng)對(duì)全球氣候變化的根本途徑。低碳經(jīng)濟(jì)實(shí)質(zhì)上就是要求單位碳排放產(chǎn)生更多的經(jīng)濟(jì)產(chǎn)出,即提高碳生產(chǎn)率。涉及地區(qū)低碳發(fā)展的影響因素很多,具有顯著空間相關(guān)性的因素和無(wú)顯著空間相關(guān)性的因素往往同時(shí)作用,在構(gòu)建模型時(shí)需要綜合考慮。本文在空間自相關(guān)方法的基礎(chǔ)上首先確定了中國(guó)省域碳生產(chǎn)率影響因素的空間相關(guān)性,其中產(chǎn)業(yè)結(jié)構(gòu)是全局變量,能源結(jié)構(gòu)、技術(shù)進(jìn)步和勞動(dòng)生產(chǎn)率均是局域變量,再通過(guò)混合地理加權(quán)回歸估計(jì)了"十一五"末和"十二五"末4個(gè)影響因素的回歸參數(shù)值并作分析。研究結(jié)果顯示:(1)能源結(jié)構(gòu)(火電比重)對(duì)于碳生產(chǎn)率具有負(fù)向影響,而產(chǎn)業(yè)結(jié)構(gòu)(服務(wù)業(yè)比重)、技術(shù)進(jìn)步(年專利授權(quán)數(shù)量)和勞動(dòng)生產(chǎn)率(單位從業(yè)人員的工業(yè)增加值)對(duì)于碳生產(chǎn)率具有正向影響;從回歸參數(shù)估計(jì)值來(lái)看,產(chǎn)業(yè)結(jié)構(gòu)的影響程度占據(jù)主導(dǎo)地位,其次是能源結(jié)構(gòu),再次是技術(shù)進(jìn)步,最后為勞動(dòng)生產(chǎn)率;(2)產(chǎn)業(yè)結(jié)構(gòu)對(duì)碳生產(chǎn)率的正向影響程度在增大,能源結(jié)構(gòu)對(duì)碳生產(chǎn)率的負(fù)向影響在空間分布上呈現(xiàn)出明顯的自南向北遞減特征,而技術(shù)進(jìn)步和勞動(dòng)生產(chǎn)率的正向影響則呈現(xiàn)出明顯的自北向南遞減特征;"十一五"末到"十二五"末,總體上能源結(jié)構(gòu)和勞動(dòng)生產(chǎn)率對(duì)碳生產(chǎn)率的影響程度在減小,而技術(shù)進(jìn)步的影響在增大。最后,提出了相關(guān)的政策建議。
[Abstract]:Low-carbon economy is the fundamental way to deal with global climate change. Low carbon economy essentially requires more economic output per unit of carbon emissions, that is, increasing carbon productivity. There are many factors affecting the development of low carbon in the region. The factors with significant spatial correlation and those without significant spatial correlation often act simultaneously, so it is necessary to consider comprehensively when building the model. Based on the spatial autocorrelation method, this paper first determines the spatial correlation of the influencing factors of China's provincial carbon productivity, in which the industrial structure is a global variable, and the energy structure, technological progress and labor productivity are local variables. The regression parameters of the four influencing factors in the end of the Eleventh Five-Year Plan and the end of the 12th Five-Year Plan were estimated and analyzed by mixed geographical weighted regression. The results show that: (1) the energy structure (specific gravity of thermal power) has a negative effect on carbon productivity. Industrial structure (proportion of service industry), technological progress (number of annual patent license) and labor productivity (industrial added value of unit employee) have positive effects on carbon productivity. The influence of industrial structure is dominant, followed by energy structure, technological progress, and finally labor productivity. (2) the positive impact of industrial structure on carbon productivity is increasing. The negative effect of energy structure on carbon productivity shows an obvious downward trend from south to north in spatial distribution. The positive effects of technological progress and labor productivity are obviously decreasing from north to south, and from the end of the 11th Five-Year Plan to the end of the 12th Five-Year Plan, the overall impact of energy structure and labor productivity on carbon productivity is decreasing. And the impact of technological progress is increasing. Finally, the relevant policy recommendations are put forward.
【作者單位】： 中國(guó)科學(xué)院區(qū)域可持續(xù)發(fā)展分析與模擬重點(diǎn)實(shí)驗(yàn)室;中國(guó)科學(xué)院地理科學(xué)與資源研究所;中國(guó)科學(xué)院大學(xué)資源與環(huán)境學(xué)院;
【基金】：中華人民共和國(guó)科學(xué)技術(shù)部國(guó)家重點(diǎn)研發(fā)計(jì)劃項(xiàng)目(2016YFA0602804) 國(guó)家自然科學(xué)基金項(xiàng)目(41430636;41571518)
【分類號(hào)】：F832.5;X196

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