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This study focuses on the analysis of optimal energy allocation and environmental performance of China's three major urban agglomerations. Specifically, this paper first uses a fixed-input DEA model to obtain the optimal allocation of energy input. Then, an evaluation model based on the optimal allocation of energy input is proposed to evaluate environmental performance. Finally, these models are applied to the empirical analysis of the three major urban agglomerations in China. This article mainly draws the following conclusions. First, energy is wasted in most cities in the Beijing-Tianjin-Hebei region. Second, from 2012 to 2016, the environmental performance of each urban agglomeration showed a downward trend and then an upward trend. Third, the overall environmental performance of the three major urban agglomerations is not high. Some specific regions, such as Handan, Anyang, Xingtai, and Baoding, behave not well in protecting the environment.

Rapid economic growth has led to increasing pollution emission, leading governments to require emission reductions by specific amounts. The allocation of specific emission reduction tasks has become a significant issue and has drawn the attention of academia. Data envelopment analysis (DEA) has been extended to construct the allocation of emission reduction tasks model. These previous DEA-based approaches have strong assumptions about individual enterprise production. In this paper, we propose a new method to accurately assess the production, using each enterprise’s previously observed production to construct its own production technology plan. With emission permits decreased, the enterprise can have new production strategy based on its own technology. Assuming emission permits can be freely bought and sold, we show how each enterprise can determine the optimal amount of emission allowance that should be used for production, which may leave some allowance to be sold for extra profit or may require the purchase of permits from other firms. Considering the limitation on the total allowance from emission permits, we introduce the concept of satisfaction degree and use it in maximising the minimum enterprise satisfaction degree. Last, a numerical example is presented and an empirical application is given to verify the proposed approach. [ABSTRACT FROM PUBLISHER]

Fire-related hazards and incidents are a very common phenomenon that affects human society heavily, so many organisations, over a long period of time, have made efforts to mitigate fires and the caused damages. It is widely acknowledged that an evaluation of fire protection performance is critical for such efforts. In this paper, we propose a new data envelopment analysis-based approach for fire protection efficiency analysis in the United States at the state level. For this purpose, the fire protection system is generalised as an innovative two-stage network process, in which the fire hazard defence subsystem in the first stage is followed by a fire incident fighting subsystem in the second stage. Further, both intermediate outputs and final outputs are all undesirable measures. The fire protection expenditure, a kind of shared resources, is modelled under managerial disposability since it can be intentionally used to reduce fires and fire damages. Based on an empirical study of data from 2010 to 2014, we find that: (1) Texas, California, New York, Florida, and Illinois are the top five states prone to fire incidents. (2) The United States as a whole has an average fire protection efficiency of 0.590, implying relatively low fire protection performances at the state level. (3) Wyoming, Vermont, and Rhode Island are the top three most efficient states, whereas Iowa, Arkansas, and Pennsylvania are ranked the least efficient. Further, (4) the fire incident fighting efficiency is more likely to be higher than the fire hazard defence efficiency for most states. (5) Looking at larger scale by grouping all states into eight areas, the Far West has the highest fire protection efficiency, while Southeast and Plains areas have the lowest efficiency scores. Interestingly, (6) the results show also that a 1% increase in the fire protection expenditure by states per capita will result in a 2.6893% improvement in fire protection efficiency. [ABSTRACT FROM AUTHOR]

China's economic reform and opening policy has resulted in China's rapid growth, especially in terms of economy. The growth, however, is mainly based on huge consumption of natural resources, and that consumption should be reduced by significant amounts to protect global ecosystems. Emerged in 1980s, “big data” has developed very quickly in recent years, bringing new perspectives and opportunities for all kinds of academic fields, especially in DEA since it is a research highly based on data. In this paper, we propose a SBM-DEA model based on natural resource input orientation to evaluate the efficiency of natural resource utilization for 26 provincial regions in mainland China from 2005 to 2012. A DEA-based approach is developed to allocate a reduced total of natural resources among the 26 provincial regions. Drawing support from the developments of “big data”, we use each provincial region's previously observed production data to characterize its own production technology, which allows a better analysis of how the production will change after allocations of natural resources are reduced. Our empirical study shows that most provincial regions perform well in terms of efficiency in utilizing natural resources, but that is based on huge consumption of natural resources. From an area perspective, the eastern area of China achieved the highest average efficiency value, followed by the central and western areas. Our study also indicates that the most effective allocation of the natural resources requires about half of the 26 provincial regions to reduce their natural resource consumption while the remaining ones can maintain their original consumption. [ABSTRACT FROM AUTHOR]

Previous studies resource allocation methods based on data envelopment analysis assume that all the assessed decision‐making units share a common production technology, and all decision‐making units become efficient after the resources are allocated. However, in the real world, production technology tends to be heterogeneous among the decision‐making units because of the differences in economic development, geographic location, and market conditions. Correspondingly, when some decision‐making units are far away from the efficient frontier, they may not become efficient easily using the resources allocated to them. In this paper, we propose a data envelopment analysis‐based approach which considers production technology heterogeneity among decision‐making units when allocating resource reduction amounts to each. In our model, the decision‐making units are divided into subgroups based on their economic development level, an important indicator directly reflecting each decision‐making unit's production technology level. Each subgroup has its specific production technology, and the decision‐making units in the same subgroup have a similar technology level, which allows better identification of how the production of those decision‐making units can change when their resource inputs change. We present an empirical example using China's mainland provinces as decision‐making units to demonstrate the practicability and applicability of our proposed model. [ABSTRACT FROM AUTHOR]