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Carbon emissions caused by the massive consumption of energy have brought enormous pressure on the Chinese government. Accurately and rapidly characterizing the spatiotemporal characteristics of Chinese city-level carbon emissions is crucial for policy decision making. Based on multi-dimensional data, including nighttime light (NTL) data, land use (LU) data, land surface temperature (LST) data, and added-value secondary industry (AVSI) data, a deep neural network ensemble (DNNE) model was built to analyze the nonlinear relationship between multi-dimensional data and province-level carbon emission statistics (CES) data. The city-level carbon emissions data were estimated, and the spatiotemporal characteristics were analyzed. As compared to the energy statistics released by partial cities, the results showed that the DNNE model based on multi-dimensional data could well estimate city-level carbon emissions data. In addition, according to a linear trend analysis and standard deviational ellipse (SDE) analysis of China from 2001 to 2019, we concluded that the spatiotemporal changes in carbon emissions at the city level were in accordance with the development of China's economy. Furthermore, the results can provide a useful reference for the scientific formulation, implementation, and evaluation of carbon emissions reduction policies.

PurposeThe purpose of this paper is to advance a proactive perspective on business exit and develop a typology of exit strategies.Design/methodology/approachThis is a research paper, which builds on extant theoretical and empirical research.FindingsBusiness exit, along with entry, is an integral part of corporate strategy that a firm could utilize to reshuffle its business portfolio and embrace new opportunities. In today's changing environment characterized by high uncertainty and high velocity, it becomes increasingly important for firms to manage business exit deliberately and proficiently. The traditional perspective which generally perceives exits as failures or responses to failures is no longer sufficient. A proactive perspective on exit could be advanced to better inform exit research and practice. Adopting the dynamic capabilities approach, this paper develops a typology of four exit strategies – retreat, redeploy, realign, and reconfigure – and examines the essential tasks of these strategies as well as the corresponding dynamic capabilities required for their successful implementation.Originality/valueThe proactive perspective advanced in this paper systematically coalesces and elaborates on extant research and formally advocates the importance and feasibility of proactive exit. The typology offered not only helps integrate the dynamic capabilities approach with exit research but also helps better inform exit practice.