Can artificial intelligence improve green economic growth? Evidence from China.

Not only has artificial intelligence changed the production methods of traditional industries; it has also presented a great opportunity for future industrial development to decouple from environmental degradation and the promotion of green economic growth. The article studies the influence of artificial intelligence on green economic growth and its mechanism. The research shows that (1) artificial intelligence can promote green economic growth in China. After accounting for spatial factors, it was found that artificial intelligence could promote local green economic growth, but had a siphon effect on neighboring green economic growth. From the perspective of dynamic effects, in the short term, artificial intelligence will not significantly dampen green economic growth in neighboring regions. In the long run, artificial intelligence will have a stronger role in promoting green economic growth, and the siphon effect on neighboring cities will be more significant. (2) As the level of human capital increases, the negative spillover effect of artificial intelligence will be significantly weakened. The promotion effect of artificial intelligence on green economic growth is relatively weak in resource-based cities. (3) Artificial intelligence has obvious attenuation characteristics on the spatial spillover effect of green economic growth, but significant influence is limited to within 200 km. (4) Artificial intelligence has the greatest impact on productivity, accounting for 30.59% in promoting green economic growth. The green innovation effect was 0.0181, accounting for 5.64%. The resource allocation effect is 0.0011, accounting for 3.44%. This paper provides policy enlightenment for promoting industrial intelligence and green economic growth.

Does the smart city policy promote the green growth of the urban economy? Evidence from China.

Urban governance is an important cornerstone in the modernization of a national governance system. The establishment of smart cities driven by digitalization will be a vital way to promote economic green and sustainable growth. By using the data of 274 prefecture-level cities in China from 2004 to 2017, we study the impact of smart city policy on economic green growth and the underlying mechanism of the impact. It is shown that the establishment of smart cities has significantly promoted the green growth of China's economy. This conclusion is further confirmed by using exogenous geographic data as instrumental variables and robustness tests, such as the quasi-experimental method of Difference in Difference with Propensity Score Matching (PAM-DID). The mechanism test shows that promoting economic growth, reducing per unit GDP energy consumption, and lowering waste emissions represent three ways for smart cities to promote green economic growth. The heterogeneity test shows that smart city policy has an obvious promotional effect on the economic green growth of both large cities and non-resource-based cities. This paper is expected to provide a reference for the urban development and economic transformation of emerging economies.

An interactive group decision model for selecting treatment schemes for mitigating air pollution.

Air pollution has caused huge losses of life and property. So, how to choose a practically effective scheme to m.itigate air pollution is of great significance. However, such a selection problem of treatment schemes represents really a group negotiation process of many decision makers (DMs), involving a variety of fuzzy information and preferences. To successfully address this selection problem, this paper proposes a novel group negotiation decision model by jointly employing various approaches, such as hesitant fuzzy set, grey target, grey incidence analysis, and graph model for conflict resolution (GMCR). Then, this model is used to determine the equilibrium schemes for treating air pollution. It is expected that this work provides a method for Chinese government to introduce programs to target air pollution control.