Previous literature has shown that manufacturers’ choices between radical and incremental green innovation modes can greatly impact the tradeoff between industry growth and carbon emission reduction. Yet, how the government can motivate manufacturers to implement radical green innovations to reduce carbon emission is unclear. In this paper, the researchers construct an evolutionary game model to analyze the joint impacts of carbon tax and innovation subsidy on manufacturers’ choices of green innovation mode. We derive the conditions for manufacturers’ stable strategies. Based on those results, we find that four factors—carbon tax, innovation subsidy, consumer green preference, and manufacturers’ capabilities of absorbing and adopting new technologies—may facilitate the choice of radical innovation. Furthermore, we conduct numerical simulations to verify the theoretical results, and further illustrate how the synergy of carbon tax rate and subsidy level affects the evolution of the green innovation mode choices. Specifically, we demonstrate the superiority of portfolio policy in the early stage of green innovation over single policy. In contrast, in the later stage, it is carbon tax but not innovation subsidy that remains effective. We discuss the insights for the government to formulate appropriate environmental policies to effectively promote the adoption of green innovation and reduce carbon emission.

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Due to the ever-changing and complex market environment, companies frequently face highly uncertain demand where data are so insufficient that the use of random or fuzzy variables, which are typically assumed in the literature, is impractical. Furthermore, companies are often risk-averse when making decisions. To address these two challenges, in this paper, we present the first study on a risk-averse newsvendor problem using the framework of uncertainty theory. To measure risk aversion, we adopt the measure of tail value-at-risk redefined based on uncertainty theory. We are able to analytically derive the optimal order quantity that maximizes the newsvendor's expected utility. We find that the optimal order quantity of a risk-averse newsvendor is less than that of a risk-neutral newsvendor. Furthermore, as the degree of risk aversion increases, the optimal order quantity decreases. Also, we show that the optimal order quantity may be independent of the risk confidence level when the degree of risk aversion is below a threshold. Moreover, we use numerical examples to illustrate how various parameters, such as the degree of risk aversion, salvage value, and unit ordering cost, affect the optimal order quantity. INDEX TERMS Newsvendor problem, uncertainty theory, uncertain variable, risk aversion.

This paper investigates the impact of government R&D support on financial growth of SMEs, and analyzes the significant different supportive effect for the enterprises with different nature of control rights. We take 516 listed companies from the SMEs' board on Shenzhen stock market as sample, using the data from 2006 to 2011 and implementing the method of Propensity Score Matching to relieve the sample selection bias. The empirical results indicate that: On the whole, government R&D support can effectively improve the financial growth of SMEs, and demonstrates an 1-2 year-lagged-effect; the property of enterprises' ultimate controller also has extraordinary impact on R&D supportive effect, financial growth of non-state-holding companies which have accepted R&D support will turn out to be more significant improvement, while that of the state-controlled enterprises have not been significantly enhanced in the short-term, or even fared-worse than the matched samples.