Evaluating Regional Carbon Inequality and Its Dependence with Carbon Efficiency: Implications for Carbon Neutrality

Abstract

This paper proposes a novel regional carbon emission inequality (RCI) index based on a special kind of general distribution. Using the proposed RCI index and based on China’s county-level panel data over the time span of 1997–2017, the regional carbon emission inequality of China is evaluated at intra-provincial, sub-national, and national levels. Based on that, the dependence between regional carbon inequality and carbon efficiency is studied by using copula functions and nonlinear dependence measures. The empirical results show that: (1) Shanghai, Tianjin, and Inner Mongolia have the worst carbon inequalities; while Hainan, Qinghai, and Jiangxi are the three most carbon-equal provinces; (2) there is a divergence phenomenon in RCI values of municipalities over the past decade; (3) from the national-level perspective, the inter-provincial carbon emission inequality is much greater than that at the intra-provincial level; (4) from the sub-national-level perspective, the east region has the highest RCI value, followed by the northeast, west, and the central regions; (5) there is a so-called "efficiency–equality (E-E) trade-off" in each provincial administrative unit, meaning that the higher carbon efficiency generally comes with higher carbon inequality, i.e., carbon efficiency comes at a price of carbon inequality; and (6) by re-grouping provincial units via the efficiency–equality cost and industrial structure, respectively, both carbon equality and carbon efficiency can be achieved in some regions simultaneously, thereby getting out of the “E-E trade-off” dilemma. The empirical evidence may provide valuable insight regarding the topic of “equality and efficiency” in environmental economics, and offer policy implications for regional economic planning and coordination.

Tail Risk Signal Detection through a Novel EGB2 Option Pricing Model

“…In the literature, EGB2 distribution has been widely used in empirical studies, such as the modeling of income distribution [27], stock returns [32], future markets' sentiment [33], currency exchange rates [34,35], and carbon emission inequality [36].…”