Forecasting the Allocative Efficiency of Carbon Emission Allowance Financial Assets in China at the Provincial Level in 2020

Abstract

Abstract:As the result of climate change and deteriorating global environmental quality, nations are under pressure to reduce their emissions of greenhouse gases per unit of GDP. China has announced that it is aiming not only to reduce carbon emission per unit of GDP, but also to consume increased amounts of non-fossil energy. The carbon emission allowance is a new type of financial asset in each Chinese province and city that also affects individual firms. This paper attempts to examine the allocative efficiency of carbon emission reduction and non-fossil energy consumption by employing a zero sum gains data envelopment analysis (ZSG-DEA) model, given the premise of fixed CO 2 emissions as well as non-fossil energy consumption. In making its forecasts, the paper optimizes allocative efficiency in 2020 using 2010 economic and carbon emission data from 30 provinces and cities across China as its baseline. An efficient allocation scheme is achieved for all the provinces and cities using the ZSG-DEA model through five iterative calculations.

Forecasting the Allocation Ratio of Carbon Emission Allowance Currency for 2020 and 2030 in China

“…Allocation based on Data Envelopment Analysis (DEA) involves allocating carbon emission allowances across different regions [12,13]. The advantage of this approach is that it can simultaneously consider several different factors.…”

“…FRCEAC(a) denotes the forecasted ratio of CO 2 emission allowance currency based on Equation (13) for 2020 in China according to the present article. FRCEAC(b) denotes the forecasted ratio of CO 2 emission allowance currency based on Zeng et al [12]. FRCEAC(c) denotes the forecasted ratio of CO 2 emission allowance currency based on Wang et al [13].…”

“…FRCEAC(b) denotes the forecasted ratio of CO2 emission allowance currency based on Zeng et al [12]. FRCEAC(c) denotes the forecasted ratio of CO2 emission allowance currency based on Wang et al [13].…”

“…Carbon emission intensity, energy intensity and total carbon emission forecast valuations for 2020 in China vary across studies [12,13,[28][29][30][31][32].…”

Incorporating lean thinking and life cycle assessment to reduce environmental impacts of plastic injection moulded products

“…In the manufacturing industry, carbon emissions are mostly produced by production processes due to their usage of energy such as electricity and fossil fuels (Upadhyayula et al, 2012). Recent studies by various authors indicate the importance of carbon emissions reduction and environmentally friendly products, for example: (i) Zeng et al (2016) forecasted the efficiency of carbon emissions and financial incentive on non-fossil energy based products; (ii) Aso and Cheung. (2015) developed a software to aid designers to design wind turbines with minimum carbon emissions and cost impacts.…”