Optimal low-carbon governance model of livestreaming supply chain based on multiple scenarios

Supply chain emissions reduction is an important way to promote the development of a low-carbon economy and address climate challenges. Although the scale of livestream shopping has demonstrated unprecedented growth globally, especially since the COVID-19 outbreak, livestreaming supply chains have also contributed significantly to carbon emissions. Currently, optimisation models for the low-carbon governance of livestreaming supply chains are relatively lacking. To address the issue of carbon emission reduction in livestreaming supply chains, this study paper proposes three low-carbon governance decision-making models based on environmental and operating costs to compare which governance model is optimal. The most suitable decision result for the policymaker and supply chain is both cost-effective and environmentally successful under the model considering carbon tax and carbon trade. The results show that 1) governance based only on carbon tax and collaborative operation will decrease the total cost of the livestreaming supply chain but increase the environmental cost. 2) Governance based only on carbon trading and collaborative operation will increase the total cost of the livestreaming supply chain, while the environmental cost will not change. 3) Under governance that combines carbon tax and carbon trading, collaborative operations can effectively reduce both the total cost and the environmental cost of livestreaming supply chains. Theoretically, our study enriches the research on the low-carbon governance of livestreaming supply chains. Moreover, the research results provide useful insights into the formulation of a low-carbon policy for livestreaming supply chains.

Is Emissions Trading Scheme (ETS) an Effective Market-Incentivized Environmental Regulation Policy? Evidence from China's Eight ETS Pilots.

Climate change and environmental issues caused by carbon emissions have attracted the attention of governments around the world. Drawing on the experience of the EU, China is actively developing a national carbon emissions trading market, trying to encourage emission entities to incorporate carbon emissions reduction into production and consumption decisions through carbon pricing. Is this scheme an effective market-incentivized environmental regulatory policy? Since China successively launched ETS pilots in 2013, the effectiveness of reducing carbon emissions has become one of the current focus issues. This study uses the difference-in-differences (DID) method to evaluate the impact of ETS implementation on emissions reduction and employs the Super-SBM model in data envelopment analysis (DEA) to evaluate the emission-reduction efficiency of eight ETS pilots in China. We find that the carbon trading policy has achieved emission-reduction effects in the implementation stage, and the greenness of economic growth has a significant positive impact on regional GDP. The establishment of China's unified carbon market should be coordinated with regional development. Some supporting measures for regional ecological compensation and the mitigation of regional development are yet to be adopted.

Energy consumption structural adjustment and carbon neutrality in the post-COVID-19 era.

Since the spread of COVID-19 pandemic all over the world, a significant recession has broken out with no precedent. China has brought up a new voluntary contribution target that achieving carbon neutrality by 2060. How to achieve climate change mitigation targets without heavily hindering economic development is of great importance in the future. In this study, a Markov chain model is employed to forecast primary energy consumption (PEC) structure and verify whether the carbon intensity target would be achieved under three scenarios with different economic growth rates, such as 6.1%, 4.2%, and 2.3%, respectively. A multi-sector dynamic stochastic general equilibrium (DSGE) model is employed to simulate and evaluate economic development, fossil and non-fossil energy consumption, and CO2 emissions under three scenarios using data calibration according to the Markov chain prediction result. The prediction results from the Markov chain show that energy structural adjustment can help us achieve the carbon intensity target of 2030 under both steady and mid-speed development scenarios. As long as the economic growth rate is higher than 4.2%, the carbon intensity target can be achieved mainly through energy consumption structural change, which provides a way to achieve the carbon neutrality target of 2060. The simulation results from the DSGE model show that energy structural adjustment can also smooth the volatility of the economic fluctuation when exogenous stochastic shocks happened.