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The purpose of this study is to analyse the role of the clean development mechanism (CDM) established by the Kyoto Protocol in channelling foreign technology to China. Appraising the experience of CDM remains of key importance when drawing lessons for the post-2012 climate regime.

Descriptive analysis of the sources and the determinants of foreign technology transfer based on the examination of 1,355 registered projects. Econometric analysis of the probability of having a foreign supplier of technology in any project.

The prominence of German firms as technology providers and the absence of a strong relationship between technology suppliers and credit buyers. The econometric analysis finds that project size and cost, project location, credit buyers’ and consultants’ characteristics, as well as technology diffusion are all relevant factors in determining the probability of having a foreign supplier of technology.

China is a particularly interesting case for analysing technology transfer in CDM projects since, after a slow start, the country has become the largest and most dynamic CDM recipient worldwide. Furthermore, the analysis of CDM projects may offer some insights into the complex web of technological links between Chinese and foreign firms.

The transfer of emission-saving technologies to developing countries is expected to play a major role in addressing environmental problems worldwide.

This study analyses the sources and determinants of international technology transfer in CDM projects in China, and offers some insights into how the characteristics of the major players and the links between them affect this phenomenon.

The authors provide new quantitative evidence of the relationship between technologies and organizational design in the context of complex one-off products. The systems that produce complex, one-off products in mature, fragmented industries such as construction lack many of the typical organizational features that researchers have deemed critical to product development success (e.g., team familiarity, frequent communication, and strong leadership). In contrast, the complexity of these products requires a diverse knowledge base that is rarely found within a single firm. The one-off nature of construction’s products further requires improvization and development by a distributed network of highly specialized teams. And because the product is complex, significant innovations in the end product require systemic shifts in the product architecture. Riitta Katila, Raymond E. Levitt and Dana Sheffer use an original, hand-collected dataset of the design and construction of 112 energy-efficient “green” buildings in the United States, combined with in-depth fieldwork, to study these questions. A key conclusion is that the mature US construction industry, with its particularly fragmented supply chain, is not well suited to implementing “systemic innovations” that require coordination across trades or stages of the project. However, project integration across specialists with the highest levels of interdependence (i.e., craft, contract integration) mitigates the knowledge and coordination problems. There are implications for research on how technology shapes organizations (and particularly how organizations shape technology), and on the supply chain configuration strategies of firms in the construction industry as well as building owners who are seeking to build the best buildings possible within their budgets.

This chapter tests theoretically and empirically the existence of a stable relationship between energy consumption and CO2 emissions. Based on microeconomics and physics, a model has been specified and applied to annual data for twenty countries, which representing 61 percent of the world’s population in 2018, over the period 1995–2015. The data are from the International Energy Agency (2019) and econometric techniques including panel data and causality tests have been used. The results indicate that there is a causal relationship between energy consumption and CO2 emissions. In general, consumers cannot directly change emissions caused by production processes, but they can act on emissions caused by their own domestic energy consumption. Approximately three quarters of domestic energy consumption is due to heating and domestic hot water consumption. Taking into account the lower emissions and the lower economic cost of the initial investment, four potential energy systems have been selected for use in heating and domestic hot water. Their social returns have been assessed across nine of the twenty countries in the sample over a lifecycle of 25 years from 2018: France, Portugal, Ireland, Spain, Iceland, Germany, United Kingdom, Morocco and the United States. Cost-benefit analysis techniques have been used for this purpose and the results indicate that the use of thermal water, where applicable, is the most socially profitable system among the proposed systems, followed by natural gas. The least socially profitable systems are those using electricity.

The malfunction variables of power stations are related to the areas of weather, physical structure, control and load behaviour. To predict temporal power failure is difficult due to their unpredictable characteristics. As high accuracy is normally required, the estimation of failures of short-term temporal prediction is highly difficult. This study presents a method for converting stochastic behaviour into a stable pattern, which can subsequently be used in a short-term estimator. For this conversion, K-means clustering is employed, followed by Long-Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) algorithms are used to perform the Short-term estimation. The environment, the operation and the generated signal factors are all simulated using mathematical models. Weather parameters and load samples have been collected as part of a data set. Monte-Carlo simulation using MATLAB programming has been used to conduct experimental estimation of failures. The estimated failures of the experiment are then compared with the actual system temporal failures and found to be in good match. Therefore, for any future power grid, there is a testbed ready to estimate the future failures.

One crucial but sometimes overlooked fact regarding the difference between observation in the cross-section and observation over time must be stated before proceeding further. Tempting though it is to draw conclusions about the dynamics of a process from cross-sectional observations taken as a snapshot of that process, it is a fallacious practice except under a very precise condition that is highly unlikely to obtain in processes of interest to the social scientist. That condition is known as ergodicity.

This paper provides a theoretical framework for application of Chance-Constrained Programming (CCP) in situations where the coefficient matrix is random and its elements are not normally distributed. Much of the CCP literature proceeds to derive deterministic equivalent in computationally implementable form on the assumption of “normality”. However, in many applications, such as air pollution control, right skewed distributions are more likely to occur. Two types of models are considered in this paper. One assumes an exponential distribution of matrix coefficients, and another one uses an empirical approach. In case of exponential distributions, it is possible to derive exact “deterministic” equivalent to the chance-constrained program. Each row of the coefficient matrix is assumed to consist of independent, exponentially distributed random variables and a simple example illustrates the complexities associated with finding a numerical solution to the associated deterministic equivalent. In our empirical approach, on the other hand, simulated data typically encountered in air pollution control are provided, and the data-driven (empirical) solution to the implicit form of deterministic equivalent is obtained. Post-optimality analyses on model results are performed and risk implications of these decisions are discussed. Conclusions are drawn and directions for future research are indicated.

This study has covered many types of solar-powered air-conditioning systems that may be used as an alternative to traditional electrically powered air-conditioning systems in order to reduce energy usage. Solar adsorption air cooling is a great alternative to traditional vapor compression air-conditioning. Solar adsorption has several advantages over traditional vapor-compression systems, including being a green cooling technology which uses solar energy to drive the cycle, using pure water as an eco-friendly HFC-free refrigerant, and being mechanically simple with only the magnetic valves as moving parts. Several advancements and breakthroughs have been developed in the area of solar adsorption air-conditioners during the previous decade. However, further study is required before this technology can be put into practise. As a result, this book chapter highlights current research that adds to the understanding of solar adsorption air-conditioning technologies, with a focus on practical research. These systems have the potential to become the next iteration of air-conditioning systems, with the benefit of lowering energy usage while using plentiful solar energy supplies to supply the cooling demand.

This empirical study explores knowledge outflows from MNE subsidiaries and its impact on subsidiary performance. We develop hypotheses derived from literature on MNE knowledge flows integrated with an organizational economics perspective on knowledge-creating MNE subsidiaries. The hypotheses are tested using a simultaneous equation model applied to a unique data set encompassing a German MNE, HeidelbergCement. Enablers and impediments of knowledge outflows are assessed to explain why subsidiary managers share their knowledge with other MNE units. Our findings suggest that knowledge outflows increase a subsidiary's performance only up to a certain point and that too much knowledge sharing is detrimental to the contributing subsidiary's performance.