مکانیسم تکامل کربن منطقه ای و رویکرد پیش بینی آن توسط تجارت کربن مطالعه موردی پکن
|کد مقاله||سال انتشار||تعداد صفحات مقاله انگلیسی||ترجمه فارسی|
|105316||2018||18 صفحه PDF||سفارش دهید|
نسخه انگلیسی مقاله همین الان قابل دانلود است.
هزینه ترجمه مقاله بر اساس تعداد کلمات مقاله انگلیسی محاسبه می شود.
این مقاله تقریباً شامل 11422 کلمه می باشد.
هزینه ترجمه مقاله توسط مترجمان با تجربه، طبق جدول زیر محاسبه می شود:
Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)
Journal : Journal of Cleaner Production, Volume 172, 20 January 2018, Pages 2793-2810
Resources and environmental issues have become the main obstacles to the global sustainable development. For example, the global warming and paroxysmal environmental problems induced by fossil energy consumption are highlighted in recent years. As a big energy consumption and carbon emission country, China has tried to establish and implement the carbon emission trading mechanism in order to adjust the economic development patterns, optimize the energy structure and fulfill the emission goals. This mechanism has played a certain role in guiding and supporting the energy saving and carbon emission reduction. With the wide popularization and acceptance of low-carbon and green development, the advantages and the benefits of regional carbon emission trading mechanism will gradually show up with more trading activities and enterprise participation. Therefore, itâs imperative to explore the carbon emission trading mechanism and provide relative suggestions for government and enterprises. For analyzing the carbon emission trading mechanism in China, the development situations of economy, energy and policy were reviewed firstly. Then, based on the direct and indirect emissions, the carbon emission measurement method was used to study the emission trends of Beijing and pilot areas. With the system dynamics analysis model, the key factors and evolution circuits influential to the carbon emission mechanism were identified from the aspects of society, energy, economy and environment. The factors were further selected by extended STIRPAT model and ridge regression model in order to construct the BP Neural Network prediction model of carbon emissions. Meanwhile, take Beijing as an example, seven different development scenarios were set to test the rational levels of carbon emissions in the next five years. At last, with the prediction and scenario analysis results, some policy advices were discussed and provided theoretical and practical references for reasonable and efficient carbon emission trading.