Efficiency In Dynamic Environments Research Articles

Research on the Carbon Emission Reduction Potential of the Ports in the Yangtze River Delta of China

Green port construction has a profound impact on China’s international climate commitment to carbon neutrality by 2050. To examine the carbon emission reduction potential of 20 major ports in the Yangtze River Delta port cluster of China, this study takes the perspective of environmental efficiency and employs dynamic exogenous variable DEA in efficiency evaluation. The results show the increasing environmental efficiency of the ports in the Yangtze River Delta, rising from 0.5116 in 2013 to 0.7276 in 2019. Also, there are significant spatial differences in efficiency. After considering exogenous variable, the average efficiency has increased from 0.5537 to 0.6276, indicating that regional green development is a critical factor in port environmental efficiency evaluation. Finally, the overall efficiency of input and output have risen, the slackness of carbon emission shows a trend of decreasing, and there is great potential for reducing carbon emissions from the perspective of port environmental efficiency. The previous studies paid less attention to dynamic efficiency, exogenous variables, and carbon emission. Therefore, this paper is the first comprehensive examination on the carbon emission reduction potential of China’s Yangtze River Delta port cluster from the perspective of dynamic environmental efficiency.

Assessing the environmental efficiency of OECD countries through the lens of ecological footprint indices

Environmental efficiency plays a crucial role in achieving sustainable economic development. This study aims to enhance the current understanding of dynamic environmental efficiency by using Data Envelopment Analysis (DEA) in conjunction with the ecological footprint index. This study evaluates 27 OECD countries' environmental performance from 2000 to 2017, employing net capital stock, labor force, and energy consumption as inputs, ecological footprint as undesirable output, and GDP as desirable output. We utilize 16 window Slack-Based Measurement DEA (SBM-DEA) models, each representing consecutive years within the observation period. Additionally, we adopt the Global Malmquist-Luenberger Index (GMLI) techniques to facilitate a simultaneous evaluation of the efficiency levels for each country. Our findings reveal that the United Kingdom and Lithuania were the most and least ecologically efficient countries among the 27 OECD countries, respectively. Over the 18-year observation period, all countries showed both progress and setbacks in environmental efficiency, with a modest overall improvement. Poland, Denmark, Slovakia, and Lithuania were the most improved countries in environmental performance, while Canada and Japan showed the most significant regressions in environmental efficiency. We highlight the need for policymakers to prioritize sustainable economic growth and consider ecological footprints when making economic decisions to enhance environmental efficiency in OECD countries. Our findings have can guide policymakers in designing effective policies and strategies to enhance environmental efficiency and promote sustainable economic development.

The impact of forestation and renewable energy utilisation on environmental efficiency in Africa

Achieving carbon neutrality is a tedious challenge confronting African countries that have witnessed mounting environmental degradation. Renewable energy consumption (REC) and forestation play a critical role in dealing with the situation. Therefore, to address this concern, this study assesses the dynamic energy or environmental efficiency of 43 sampled African countries by proffering a new dynamic meta-frontier DEA model from 2010 to 2018. The bootstrap truncation regression model investigates the influence of forestation and REC on environmental efficiency in Africa. The dynamic environmental efficiency of the 43 concerned African countries is low (0.59), indicating vast room for improvement. The heterogeneity of dynamic environmental efficiency across the income groups is evident. The upper-middle-income group (UMIG) had the best performance, followed by the low-middle-income group (LMIG) and the low-income group (LIG). The technology gap ratio also confirmed the existence of a huge gap across the income groups in Africa. The bootstrap truncation regression results confirmed a U-shaped relationship between economic growth and dynamic environmental efficiency in Africa. Forestation and REC positively correlate to dynamic CO2 emission and energy efficiency in Africa. In contrast, financial development is negatively associated with CO2 emissions and Africa’s energy efficiency. The study’s findings will aid the sampled African countries in their quest to attain carbon neutrality, thereby promoting sustainability on the African continent.

Backward Private Searchable Symmetric Encryption with Improved Locality

Searchable symmetric encryption (SSE) enables clients to outsource their encrypted documents into a remote server and allows them to search the outsourced data efficiently without violating the privacy of the documents and search queries. Dynamic SSE schemes (DSSE) include performing update queries, where documents can be added or removed at the expense of leaking more information to the server. Two important privacy notions are addressed in DSSE schemes: forward and backward privacy. The first one prevents associating the newly added documents with previously issued search queries. While the second one ensures that the deleted documents cannot be linked with subsequent search queries. Backward has three formal types of leakage ordered from strong to weak security: Type-I, Type-II, and Type-III. In this paper, we propose a new DSSE scheme that achieves Type-II backward and forward privacy by generating fresh keys for each search query and preventing the server from learning the underlying operation (del or add) included in update query. Our scheme improves I/O performance and search cost. We implement our scheme and compare its efficiency against the most efficient backward privacy DSSE schemes in the literature of the same leakage: MITRA and MITRA*. Results show that our scheme outperforms the previous schemes in terms of efficiency in dynamic environments. In our experiments, the server takes 699ms to search and return (100,000) results.

Cloud-Based Multi-Robot Path Planning in Complex and Crowded Environment Using Fuzzy Logic and Online Learning

This paper considers an autonomous cloud-based multi-robot system designed to execute highly repetitive tasksin a dynamic environment such as a modern megastore. Cloud level is intended for performing the most demandingoperations in order to unload the robots that are users of cloud services in this architecture. For path planningon global level D* Lite algorithm is applied, bearing in mind its high efficiency in dynamic environments. In orderto introduce smart cost map for further improvement of path planning in complex and crowded environment, implementationof fuzzy inference system and learning algorithm is proposed. The results indicate the possibility ofapplying a similar concept in different real-world robotics applications, in order to reduce the total paths length,as well as to minimize the risk in path planning related to the human-robot interactions.

Analyzing the role of competition and cooperation in airline environmental efficiency through two dynamic environmental cross-efficiency models

We build a dynamic structure for airline environmental efficiency and propose the Dynamic Benevolent Environmental DEA Cross-efficiency model and Dynamic Aggressive Environmental DEA Cross-efficiency model to explore whether cooperation or competition can reflect the airline dynamic environmental efficiency in the past few years. Then, the models are applied to evaluate the efficiencies of 29 international airlines from 2010 to 2016. The results demonstrate that cooperation is more likely to unfold the benchmarking airlines than the competition, but competition is more likely to embody the airline with the lowest environmental efficiency.

Dynamic environmental efficiency analysis of China's power generation enterprises: a game cross-Malmquist index approach.

Since the power unbundling reform in 2002, China's power industry has been a typical sector moving towards marketization. The supply-side structural reform that began in 2015 has intensified the competition among China's power generation enterprises. In this context, this paper combines a data envelopment analysis (DEA) game cross-efficiency model with the Malmquist index approach to investigate the dynamic environmental efficiency of China's power generation enterprises in a competitive market. Furthermore, the system generalized method of moments (SYS-GMM) is employed to analyze the influences of factors on the dynamic environmental efficiency. The results show that: (1) Compared with the basic DEA Charnes-Cooper-Rhodes (CCR)-Malmquist index model, the DEA game cross-Malmquist index approach can enhance the discrimination among the decision making units (DMUs). (2) The change in the overall dynamic environmental efficiency of China's power generation enterprises mainly comes from the catch-up effect, while technological progress has not played a role as significant as expected. (3) The installed capacity growth rate, implementation of carbon emission trading pilots, economic growth rate, and enterprise ownership all affect the dynamic environmental performance of China's power generation enterprises during the study period.

A cross efficiency distinguishing method to explore the cooperation degree in dynamic airline environmental efficiency

The aviation industry's carbon emissions have aroused a wide concern as the global airlines had discharged about 915 million tons CO2 in 2019. The airlines have taken many measures to control emissions. However, existing studies have not considered the role of cooperation or competition in evaluating the effects of controlling emissions. We build a dynamic structure for airline environmental efficiency and two Dynamic Benevolent Environmental DEA Cross-efficiency model and Dynamic Aggressive Environmental DEA Cross-efficiency model to evaluate the airline dynamic environmental efficiency. Then we apply a cross efficiency distinguishing method to reflect the degree of cooperation and competition in airline environmental efficiency. Then, the models are applied to assess the efficiency of 29 global airlines from 2010 to 2016. The empirical analysis results confirm the reasonability of the models. The results show that most airlines have a relationship in which the cooperation is larger than the competition in 2010 and 2012, but they have a relationship that both competition and cooperation coexist in other years.

Potential Field Method Parameters Tuning Using Fuzzy Inference System for Adaptive Formation Control of Multi-Mobile Robots

Nowadays, employing more than one single robot in complex tasks or dangerous environments is highly required. Thus, the formation of multi-mobile robots is an active field. One famous method for formation control is the Potential Field Method due to its simplicity and efficiency in dynamic environments. Therefore, we propose a Fuzzy Inference tuning of the potential field parameters to overcome its limitations. We implement the modified method with tuned parameters on MATLAB and apply it to three TurtleBot3 burger model robots. Then, several real-time experiments are carried out to confirm the applicability and validity of the modified potential filed method to achieve the robots’ tasks. The results assert that the TurtleBot3 robots can escape from a local minimum, pass through a narrow passage, and pass between two closely placed obstacles.

Reinforcement learning enabled cooperative spectrum sensing in cognitive radio networks

In cognitive radio (CR) networks, fast and accurate spectrum sensing plays a fundamental role in achieving high spectral efficiency. In this paper, a reinforcement learning (RL) enabled cooperative spectrum sensing scheme is proposed for the secondary users (SUs) to determine the scanning order of channels and select the partner for cooperative spectrum sensing. By applying Q-learning approach, each SU learns the occupancy pattern of the primary channels thus forming a dynamic scanning preference list, so as to reduce the scanning overhead and access delay. To improve the detection efficiency in dynamic environment, a discounted upper confidence bound (D-UCB) based cooperation partner selection algorithm is devised wherein each SU learns the time varying detection probability of its neighbors, and selects the one with the potentially highest detection probability as the cooperation partner. Simulation results demonstrate that the proposed cooperative spectrum sensing scheme achieves significant performance gain over various reference algorithms in terms of scanning overhead, access delay, and detection efficiency.

Dynamic Environmental Efficiency Assessment of Industrial Water Pollution

In the face of severe water pollution, all provinces and cities in China have actively invested in water environment management funds driven by the goals of national energy conservation and emissions reduction. However, due to differences in natural environment, economic and technological levels, industrial structure, and other aspects in provinces and cities, their water environment management effects are also different across time and space. Under economic development and environmental regulation policies, it can be seen that the change in industrial GDP is not completely consistent with that of industrial wastewater discharge. How to improve desirable outputs and reduce undesirable outputs under the limited investment in water pollution control are key issues when investigating the efficiency of industrial water pollution control. This study uses the Dynamic SBM (Slacks-Based Measure) model to assess wastewater resources for research samples covering the 30 regions of China. There are two output variables, two input variables, and one carry-over variable. The output variables are industrial wastewater treatment and industrial output, the two input variables are industrial water consumption and facility operation cost, and the carry-over variable is industrial waste. This study concludes with implications for theory research, as these variables may lead to a better understanding and merging with the input variables, output variables, and carry-over variable of recent studies. The empirical results show that from the efficiency rank changes of the 30 regions for 2011–2015, regions with higher industrial output do not appear to have improved versus other regions, such as for Shandong, Guangdong, Jiangsu, Qinghai, and Zhejiang. The 30 regions’ efficiency scores show some volatility, with 13 regions’ efficiency score volatility clustering close to 0, like Beijing, Chongqing, Shandong, Guangdong, and Sichuan. In contrast, for Anhui, Inner Mongolia, Zhejiang, and Xinjiang, their efficiency scores fell more than other regions in this period and thus should adjust their input/output variables to increase their efficiency scores. This study further presents that many lower-/middle-/high-industrial output regions do not achieve a balance between industrial output and industrial wastewater treatment. How to find a balance between these two factors for any region is a vitally important issue for industrial wastewater treatment policy makers. Under such a circumstance, an industrial output region may not actually be highly efficient at doing this.

Dynamic environmental efficiency assessment for wastewater treatment plants

Purpose Life cycle assessment (LCA) and data envelopment analysis (DEA) have been combined in numerous occasions in order to identify the environmental efficiency of multiple units. In many cases, important differences in environmental performance have been identified among the units, referred to as decision-making units (DMUs). However, most studies have been limited to 1 year of assessment, preventing the study from identifying if efficiency values were linked to a specific trend of each DMU through time or to random annual changes. Therefore, the current study delves into the temporal variations in efficiency using DEA.

Dynamic performance assessment of a supply chain process

Purpose – A variety of tools are available to measure supply chain efficiency, but there are a few methods available for assessing efficiency in dynamic environments. The purpose of this paper is to illustrate the use of data envelopment analysis (DEA) with the help vector auto regression in measuring internal supply chain performance in dynamic environment. Design/methodology/approach – Two DEA models were developed – the static DEA that is traditional DEA methodology and the dynamic DEA. The models are further enhanced with scenario analysis to derive more meaningful business insights for managers in making benchmarking and resource planning decisions. Findings – The results demonstrate that lagged effects can lead to changes in efficiency scores, rankings, and efficiency classification. So, using static DEA models in dynamic environment can be potentially misleading. Using impulse response analysis it has been seen that shocks given to marketing strategy in MR affects more at each of the decision-making unit’s (DMU’s) compared to other variables, further the authors could also investigate the dependent variables (output) shocks to input variables. Social implications – Methodology can be applied to a wide range of evaluation problems in place of conventional DEA models. Results show that lagged effects can lead to substantial discrepancies in evaluation results. Biased evaluation results would easily lead to erroneous decision and policy making for the firm. Therefore the authors should always take a broader perspective in evaluating longitudinal performance by incorporating the effects into evaluation and decision-making processes. Future work of this study could look into the possibility of modeling in a stochastic supply chain environment. In addition, it will also be interesting to look into evaluating the stochastic DEA model in multiple time periods in order to examine whether there is any technological influence on the supply chain efficiency. Originality/value – The contribution of this study provides useful insights into the use of DDEA as a modeling tool to aid managerial decision making in assessing supply chain efficiency in dynamic environment.

Dynamic environmental efficiency evaluation of electric power industries: Evidence from OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries

As most power is consumed domestically, the environmental efficiencies of electric power industries in different countries may serve as a benchmark to evaluate their emission reduction efforts. Taking the power generated and carbon emissions as desirable and undesirable outputs respectively, this study adopts the environmental Malmquist index, which is based on a SBM-DEA (slack based measure data envelopment analysis) model, to investigate the environmental TFP (total factor productivity) index of electric power industries in 26 OECD (Organization for Economic Cooperation and Development) and BRIC (Brazil, Russia, India and China) countries from 1996 to 2010. In addition, we employ the Tobit regression model to analyze the influence of related factors on TFP and its decompositions. The empirical results indicate that the dynamic environmental efficiency or TFP provides a good perspective for evaluating emission reduction efforts of electric power industries in different countries. Fuel structure change and technological progress are the main driving forces to promote dynamic environmental efficiency. Furthermore, the economic situation and energy price changes also affect dynamic environmental efficiency significantly. Therefore, taking the energy structure and affordability into consideration, environmental TFP may indicate climate change mitigation efforts to a large extent.