Conventional methods are less robust in terms of accurately forecasting non-stationary and nonlineary carbon prices. In this study, we propose an empirical mode decomposition-based evolutionary least squares support vector regression multiscale ensemble forecasting model for carbon price forecasting. Firstly, each carbon price is disassembled into several simple modes with high stability and high regularity via empirical mode decomposition. Secondly, particle swarm optimization-based evolutionary least squares support vector regression is used to forecast each mode. Thirdly, the forecasted values of all the modes are composed into the ones of the original carbon price. Finally, using four different-matured carbon futures prices under the European Union Emissions Trading Scheme as samples, the empirical results show that the proposed model is more robust than the other popular forecasting methods in terms of statistical measures and trading performances.

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Based on a the micro dataset set of private Chinese-listed companies, this study examines the in detail the impact of hometown connections between chief executive officers (CEOs) and chairmen on CEO compensation–performance sensitivity. The empirical results suggest that a hometown connection prompts both the chairman and the CEO to pay more attention to their reputations, which improves CEO compensation–performance sensitivity. This hometown effect is more pronounced when the hometown culture is strong and the degree of marketization is high. The mechanism test reveals that an externally hierd CEO strengthens the influence of a hometown connection on CEO compensation–performance sensitivity, however, this influence weakens with increasing CEO tenure. Overall, this study enriches the literature on informal institutions and compensation contracts and provides valuable insights for company managers and policymakers in emerging markets.