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To find out whether carbon sequestration is both effective at mitigating climate change and promoting economic growth, in this paper, by adopting a stochastic frontier panel model with translog production function, carbon sequestration is incorporated into endogenous variables to establish estimation model of carbon sequestration total factor productivity (CSTFP) and examine CSTFP growth and its drivers decomposition of the Yangtze River Economic Belt (YREB) of China in three estimations. The result shows that, (1) compared to traditional TFP growth, CSTFP growth in YREB is improved by 26.74 percentages (from −26.55% to 0.20%), contributed by three positive drivers of technical efficiency change (28.59%), technological progress change (18.55%), and scale efficiency change (3.99%); (2) different CSTFP growth exists in three watershed segments of YREB, which firstly is the upper reaches (0.62%), then the lower reaches (0.11%) and the middle reaches (−0.14%). Improved CSTFP growth owes to carbon sequestration’s harmonious symbiosis where natural ecosystems and human activities are naturally blended while insufficient synergies are bottleneck for promotion of CSTFP growth in YREB. Related policy suggestions are provided in the end. The proposed analysis framework is efficient to disclose CSTFP growth in YREB, and can also be applied to similar analysis on CSTFP in regions and extended to multi-country/region analysis.

The purpose of this paper is to measure the relative efficiency of non-life insurance companies in India from 2008 to 2013 by using Stochastic Frontier Analysis. It is found that BharatiAXA is at rank one and it has a relative efficiency score higher than all other companies. Since γ = 0.9251 which is close to one indicates that all the residual variations are due to the inefficiency effects. It is also found that the mean of efficiency score of non-life insurance companies is increasing from year to year.

This paper adopts STIRPAT model with ridge regression to estimate factor influence on energy carbon emissions with the case of the upper reaches of the Yangtze River of China. The result shows that seven factors have positive but different influence on energy carbon emissions, which impact coefficient of population is 5.559, and respectively is secondary industry value-added (1.054), CO2 emissions intensity (0.597), productivity (0.416), GDP per capita (0.352), urban employment (0.309) and urbanization (0.221). For improvement of factor influence on energy carbon emissions, it is urgent to promote industrial low carbon transition, encourage R&D of low carbon technologies for clean production and advocate low carbon urbanization and life.