The comprehensive environmental efficiency of socioeconomic sectors in China: An analysis based on a non-separable bad output SBM

The increasingly high frequency of heavy air pollution in most regions of China signals the urgent need for the transition to an environmentally friendly production performance by socioeconomic sectors for the sake of people's health and sustainable development. Focusing on CO2 and major air pollutants, this paper presents a comprehensive environmental efficiency index based on evaluating the environmental efficiency of major socioeconomic sectors, including agriculture, power, industry, residential and transportation, at the province level in China in 2010 based on a slack-based measure DEA model with non-separable bad output and weights determined by the coefficient of variation method. In terms of the environment, 5, 16, 6, 7 and 4 provinces operated along the production frontier for the agricultural, power, industrial, residential and transportation sectors, respectively, in China in 2010, whereas Shanxi, Heilongjiang, Ningxia, Hubei and Yunnan showed lowest efficiency correspondingly. The comprehensive environmental efficiency index varied from 0.3863 to 0.9261 for 30 provinces in China, with a nationwide average of 0.6383 in 2010; Shanghai ranked at the top, and Shanxi was last. Regional disparities in environmental efficiency were identified. A more detailed inefficiency decomposition and benchmarking analysis provided insight for understanding the source of comprehensive environmental inefficiency and, more specifically, the reduction potential for CO2 and air pollutants. Some specific research and policy implications were uncovered from this work

Carbon emissions in China's thermal electricity and heating industry: An input-output structural decomposition analysis

CO2 emissions from China accounted for 27 per cent of global emisions in 2019. More than one third of China's CO2 emissions come from the thermal electricity and heating sector. Unfortunately, this area has received limited academic attention. This research aims to find the key drivers of CO2 emissions in the thermal electricity and heating sector, as well as investigating how energy policies affect those drivers. We use data from 2007 to 2018 to decompose the drivers of CO2 emissions into four types, namely: energy structure; energy intensity; input-output structure; and the demand for electricity and heating. We find that the demand for electricity and heating is the main driver of the increase in CO2 emissions, and energy intensity has a slight effect on increasing carbon emissions. Improving the input-output structure can significantly help to reduce CO2 emissions, but optimising the energy structure only has a limited influence. This study complements the existing literature and finds that the continuous upgrading of power generation technology is less effective at reducing emissions and needs to be accompanied by the market reform of thermal power prices. Second, this study extends the research on CO2 emissions and enriches the application of the IO-SDA method. In terms of policy implications, we suggest that energy policies should be more flexible and adaptive to the varying socio-economic conditions in different cities and provinces in China. Accelerating the market-oriented reforms with regard to electricity pricing is also important if the benefits of technology upgrading and innovation are to be realised

Intellectual Property Rights

This edited volume, Intellectual Property Rights – Patent, is a collection of reviewed and relevant research chapters, offering a comprehensive overview of recent developments in the field of patents and its issues. The book comprises chapters authored by various researchers and edited by experts active in the pharmaceutical research area. All chapters are complete in itself but united under a common research study topic. This publication aims to provide a thorough overview of the latest research efforts on patenting and the related issues for legal experts and the scientific community and open new possible research paths for further novel developments

A Bayesian stochastic frontier analysis of Chinese fossil-fuel electricity generation companies

This paper analyses the technical efficiency of Chinese fossil-fuel electricity generation companies from 1999 to 2011, using a Bayesian stochastic frontier model. The results reveal that efficiency varies among the fossil-fuel electricity generation companies that were analysed. We also focus on the factors of size, location, government ownership and mixed sources of electricity generation for the fossil-fuel electricity generation companies, and also examine their effects on the efficiency of these companies. Policy implications are derived.info:eu-repo/semantics/publishedVersio

Multiperiod modelling planning and productivity and energy efficient assessment of an industrial gases facility

El creixement de la demanda energètica i el continu desenvolupament tecnològic de la societat estan sobrepassant els límits mediambientals del nostre planeta. Sense les mesures adequades, aquesta situació podria derivar en importants problemes mediambientals que causarien danys irreversibles al medi ambient i al benestar de la humanitat. El sector industrial és el principal consumidor energètic, amb una demanda al voltant d’un terç de la global, un aspecte que té un evident efecte negatiu amb l’impacte mediambiental. Per tant, el repte de mitigar el canvi climàtic implicarà millores en l’ús de la energia a la industria, generant grans oportunitats d’estalvi energètic i reduint el seu impacte mediambiental. En aquest sentit, es essencial obtenir informació derivada de la investigació i l’anàlisi científic que permeti desenvolupar solucions focalitzades en la reducció de costos energètics. Aquesta tesis ha tractat les necessitats particulars de la producció de gasos industrials, creant eines basades en l’optimització matemàtica que permeten una presa de decisions operatives més àgil i efectiva i detectant àrees per la millora energètica. Aquestes eines fomenten i avancen cap a una industria més eficient que permeti un futur més sostenible. Aquesta tesis té dos contribucions principals. D’una banda, s’ha desenvolupat una eina d’optimització multiperiod que permet obtenir la configuració d’operació òptima (des del punt de vista econòmic i energètic) d’un procés de producció de gasos industrials, tenint en compte totes les seves variables. Per altra banda, s’utilitza la metodologia de Data Envelopment Analisis per a comparar diferents unitats de producció de gasos industrials, identificant els focus d’ineficiència i fent recomanacions per a resoldre’ls.El crecimiento de la demanda energética y el continuo desarrollo tecnológico de la sociedad están sobrepasando los límites medioambientales de nuestro planeta. Sin las medidas adecuadas, esta situación puede derivar en importantes problemas medioambientales que podrían causar daños irreversibles al medioambiente y al bienestar de la humanidad. El sector industrial es el principal consumidor energético, consumiendo alrededor de un tercio de la demanda energética global, lo que tiene una evidente relación negativa con el impacto ambiental. Por lo tanto, el reto de mitigar el cambio climático implicará mejoras del uso de la energía en la industria, creando grandes oportunidades de ahorro energético y reduciendo su impacto ambiental. Para ello, es esencial obtener información derivada de la investigación y el análisis científico que permita desarrollar soluciones enfocadas a la reducción de costes energéticos. Esta tesis ha tratado las necesidades particulares de la producción de gases industriales, creando herramientas basadas en la optimización matemática que permiten una toma de decisiones operativas más ágil y efectiva y detectando áreas para la mejora energética. Estas herramientas fomentan y avanzan hacia una industria más eficiente que permita un futuro más sostenible. Esta tesis tiene dos contribuciones principales. Por un lado, se crea una herramienta de optimización multiperiodo que permite obtener la configuración de operación óptima (desde el punto de vista económico y energético) de un proceso de producción de gases industriales, teniendo en cuenta todas sus variables. Por otro lado, se usa la metodología de Data Envelopment Analysis para comparar diferentes unidades de producción de gases industriales, identificando los focos de ineficiencia y haciendo recomendaciones para resolverlos. En definitiva, esta tesis ofrece un conjunto de herramientas prácticas y efectivas que apoyan el proceso de toma de decisiones en actividades industriales y permiten la identificación de oportunidades de mejora energética.The growth of energy demand and the continuous technological development of society are surpassing the environmental limits of our planet. Without adequate measures, this situation can lead to serious environmental problems that could cause irreversible damage to the environment and the well-being of humanity. The industrial sector is the largest energy consumer, with about one-third of global energy demand, which has an evident negative relationship with environmental impact. Therefore, the challenge of mitigating climate change will imply improvements in the energy use in industry, creating great opportunities for energy savings and reducing its environmental impact. In this sense, it is essential to obtain information derived from research and scientific analysis that allows developing solutions focused on the reduction of energy costs. This thesis has dealt with the particular needs of the production of industrial gases, by creating tools based on mathematical optimization models that allow much more agile and effective operational decision-making as well as the detection of areas for energy improvement. These tools encourage and move towards a more efficient industry that allow a more sustainable future. Two main contributions are derived from this thesis. On the one hand, it creates a multiperiod optimization tool that allows obtaining the optimal operational configuration (from the economic and energetic points of view) of an industrial gas manufacturing process, taking into account all the variables that affect the system. On the other hand, the Data Envelopment Analysis methodology is used to compare different industrial gas production units, identifying inefficiency sources and making recommendations to adopt the best practices to solve them. Summarizing, this thesis offers a set of practical and effective tools that support the decision making process in industrial activities and allows the identification of opportunities for energy improvement

Operational performance measurement of world major airlines with a particular emphasis of Ethiopian airlines : an integrated comparative approach

Organizations specifically the airlines industry are increasingly facing the challenges of operational efficiency measurement. During the last years enormous attention has been given to the assessment and improvement of the performance of productive systems. However, literatures show that there are limitations of the existing models to measure efficiency uniformly and exhaustively across the airlines. The problems are due to lack of the technical efficiency measuring model which unifies and integrates different measuring models into a single model.Therefore, this thesis investigates assessment of the operational performance of world major airlines by employing integrated comparative models to address the above problems. In this study, technical efficiency is addressed among many performance issues by using three types of modes of performance measurement: a non parametric one, represented by Data Envelopment Analysis (DEA) and; a parametric one, represented by Stochastic Frontier Analysis (SFA) and the Balance Scorecard (BSC) which is a strategic management tools. Unlike most of the previous studies, this study integrates the BSC concepts into DEA and SFA model. To evaluate technical efficiency of major international airlines, the study use panel of unbalanced data for the year 2007-2014 to make integrated comparative analysis. The research project incorporates seven leading variables and four lagging variables taken from BSC concept to implement into the DEA and SFA. All the three models of performance measurements have their own strength and limitation if they are used alone. But if the three models are integrated and combined together, they would yield better comparative and quality of efficiency assessment. Therefore, the study primarily developed a model beginning from the theoretical framework assumption into building of a unified comparative model of integrated comparative operational efficiency assessment of airlines. The research design and methodology uses secondary data collection i.e. annual reports and business reports of airlines which are collected from the airlines own website. The huge amount of financial and operational data cannot be collected by using primary data collection method as it would make it practically impossible and expensive. So by employing secondary data collection method saves time, money and a panel data can be accessed and generated easily. Hence, from 100 world major airlines population which are ranked by revenue, simple random sampling is used to select 80 samples airlines for this study. First, the BSC identifies the input and output variables. Next, the DEA model ranks the efficiency measurement, identifies the slack variables and benchmarks the airlines. Third, the SFA model identifies technical efficiency, the random error and technical inefficiency. Finally, the technical efficiency estimates obtained from the two techniques are analyzed comparatively. The research makes further analysis of particular case of the Ethiopian Airlines in relation to the most efficient and inefficient airlines and in comparison of the regional analysis. After extensive tests have been conducted, ‘Balanced Frontier Envelopment’ model is developed. According to this model, it is a paramount to measure efficiency with combining the strength of three models together and gives better results than the previous one or two combined models. The developed and integrated strategic model enhances measuring of the operating technical efficiency of airlines. This model benefit the airlines industry in many ways such as minimizing the cost and maximizing profit through managing technical efficiency which lead into the success of the airlines. From the model perspective, therefore, result of DEA model is much higher than the result of SFA model. DEA model is easier to manipulate than the SFA model because the former does not need the functional form while the later requires a functional form. Furthermore, according to the efficiency finding of the study, first, the European regional airlines are relatively more efficient than the rest of regions in the world. Second, the North America regional airlines are the second more efficient regional airlines in the world. Third, the Ethiopian airlines are the most efficient in Africa when we compare among Egyptair, Kenyan Airways and South African Airways. Fourth, high revenue does not necessarily leads to the technical efficiency of the firm.Business ManagementD.B.L. (Business Leadership

Essays in financial technology: banking efficiency and application of machine learning models in Supply Chain Finance and credit risk assessment

The financial landscape is undergoing a significant transformation, driven by technological innovations that are reshaping traditional banking practices. This thesis examines the evolving relationship between financial technology (FinTech) and banking, specifically addressing the credit risk aspects within the domains of Supply Chain Finance (SCF) and peer-to-peer (P2P) lending. FinTech has experienced rapid growth and innovation over the past decade. It encompasses a wide range of technologies and services that aim to enhance and streamline financial processes, disrupt traditional banking models, and offer new solutions to consumers and businesses. The status of FinTech and banking is assessed through an extensive review of the current literature and empirical data. Accordingly, FinTech development has significantly impacted the financial landscape, driving innovation, competition, and customer expectations while it has exposed inefficiencies within traditional banking, it has also compelled banks to evolve and embrace technological advancements. The impact of FinTech on traditional banking models, customer behaviours, and market competition is aimed to be explored. This investigation highlights the challenges and opportunities that arise as FinTech disrupts and reshapes the banking sector, emphasizing its potential to enhance efficiency, accessibility, and customer experiences. As Chapter 3 focuses on an empirical analysis of the impact of FinTech on the operating efficiency of commercial banks in China. Further, in the context of credit risk, the thesis focuses on SCF and P2P lending, two prominent areas influenced by FinTech innovation. SCF has witnessed substantial transformation with the infusion of FinTech solutions. Digital platforms have streamlined the flow of funds within complex supply networks, enhancing the liquidity of suppliers and optimizing working capital for buyers. However, this transformation introduces new credit risk challenges. As suppliers' financial data becomes more accessible, the need for accurate risk assessment and predictive modelling becomes paramount. The integration of big data analytics, machine learning, and artificial intelligence (AI) holds the promise of refining credit risk evaluation by offering real-time insights into supplier financial health, thereby improving lending decisions and reducing defaults. Similarly, P2P lending has redefined the borrowing and lending landscape, enabling direct connections between individual borrowers and lenders. While P2P lending platforms offer speed, convenience, and access to credit for previously underserved segments, they also grapple with credit risk concerns. Evaluating the creditworthiness of individual borrowers without sufficient credit history demands innovative risk assessment methodologies. The emergence of data issues, such as imbalanced data issues, feature selection, and data processing, presents challenges in building accurate credit risk profiles for P2P lending participants. FinTech solutions play a pivotal role in creating and implementing these alternative risk assessment models. Note that, few studies in the literature investigate the benchmark of the advanced method of solving the credit risk assessment in emerging financial services. This thesis aims to address this research gap by evaluating the effectiveness of credit risk assessment models in these FinTech-driven contexts, considering both traditional methodologies and novel data-driven approaches. Chapter 4 investigates the credit risk assessment issue in Digital Supply Chain Finance (DSCF) with the Machine Learning approach and Chapter 5 emphasises the issue of data imbalance of credit risk assessment in P2P Lending. By addressing these gaps and issues, this thesis aims to contribute to the broader discourse on FinTech's role in shaping the future of banking. The findings have implications for financial institutions, policymakers, and regulators seeking to harness the benefits of FinTech while mitigating associated risks. Ultimately, this study offers insights into navigating the evolving landscape of credit risk in SCF and P2P lending within the context of an increasingly technology-driven financial ecosystem

Green Technologies for Production Processes

This book focuses on original research works about Green Technologies for Production Processes, including discrete production processes and process production processes, from various aspects that tackle product, process, and system issues in production. The aim is to report the state-of-the-art on relevant research topics and highlight the barriers, challenges, and opportunities we are facing. This book includes 22 research papers and involves energy-saving and waste reduction in production processes, design and manufacturing of green products, low carbon manufacturing and remanufacturing, management and policy for sustainable production, technologies of mitigating CO2 emissions, and other green technologies