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We review previous research on intergenerational conflict, focusing on the practical implications of this research for organizational leaders. We explain how the interaction between the interpersonal and intertemporal dimensions of intergenerational decisions creates the unique psychology of intergenerational decision-making behavior. In addition, we review the boundary conditions that have characterized much of the previous research in this area, and we examine the potential effects of loosening these constraints. Our proposals for future research include examination of the effect of intra-generational decision making on intergenerational beneficence, consideration of the role of third parties and linkage issues, investigation of the effects of intergenerational communications and negotiation when generations can interact, examination of the role of social power in influencing intergenerational interactions, investigation of the interaction between temporal construal and immortality striving, and exploration of the ways in which present decision makers detect and define the intergenerational dilemmas in their social environments.

The decarbonization of power generation is key to achieving carbon neutrality in China by the end of 2060. This paper aims to examine how green finance influences China’s low-carbon transition of power generation. Using a provincial panel data set as an empirical study example, green finance is assessed first, then empirically analyses the influences of green finance on the low-carbon transition of power generation, as well as intermediary mechanisms at play. Finally, this paper makes relevant recommendations for peak carbon and carbon neutrality in China.

To begin with, an evaluation index system with five indicators is constructed with entropy weighting method. Second, this paper uses the share of coal-fired power generation that takes in total power generation as an inverse indicator to measure the low-carbon transition in power generation. Finally, the authors perform generalized method of moments (GMM) econometric model to examine how green finance influences China’s low-carbon transition of power generation by taking advantage of 30 provincial panel data sets, spanning the period of 2007–2019. Meanwhile, the implementation of the 2016 Guidance on Green Finance is used as a turning point to address endogeneity using difference-in-difference method (DID).

The prosperity of green finance can markedly reduce the share of thermal power generation in total electricity generation, which implies a trend toward China’s low-carbon transformation in the power generation industry. Urbanization and R&D investment are driving forces influencing low-carbon transition, while economic development hinders the low-carbon transition. The conclusions remain robust after a series of tests such as the DID method, instrumental variable method and replacement indicators. Notably, the results of the mechanism analysis suggest that green finance contributes to low-carbon transformation in power generation by reducing secondary sectoral share, reducing the production of export products, promoting the advancement of green technologies and expanding the proportion of new installed capacity of renewable energy.

This paper puts forward relevant suggestions for promoting the green finance development with countermeasures such as allowing low interest rate for renewable energy power generation, facilitating market function and using carbon trade market. Additional policy implication is to promote high quality urbanization and increase R&D investment while pursuing high quality economic development. The last implication is to develop mechanism to strengthen the transformation of industrial structure, to promote high quality trade from high carbon manufactured products to low-carbon products, to stimulate more investment in green technology innovation and to accelerate the greening of installed structure in power generation industry.

This paper first attempts to examine the low-carbon transition in power generation from a new perspective of green finance. Second, this paper analyses the mechanism through several aspects: the share of secondary industry, the output of exported products, advances in green technology and the share of renewable energy in new installed capacity, which has not yet been done. Finally, this study constructs a system of indicators to evaluate green finance, including five indicators with entropy weighting method. In conclusion, this paper provides scientific references for sustainable development in China, and meanwhile for other developing countries with similar characteristics.

Coal and power generation are related upstream and downstream industries. Coal price marketization and electricity price regulation have caused the price of coal to be sensitive to the benefits of generators. The paper aims to discuss these issues.

As a financial tool, contracts for differences can both help balance interests and reduce risks caused by spot price fluctuation. This thesis regards coal demand as a triangular fuzzy stochastic variable while directing a levelling consideration towards risk returns for coal and power enterprises that are involved in coal generation contracts for differences. Risk and benefit measurement models were established between coal suppliers and power generators, and risk and benefit balance optimization models for contract negotiation were constructed.

A numerical example showed that the above models can be effectively used to avoid the risks of coal-electricity parties.

This thesis regards coal demand as a triangular fuzzy random variable while directing a levelling consideration towards the risk return to coal and power enterprises that are involved with coal generation contracts for differences. The features of this thesis are the following: demand information is regarded as a fuzzy random variable instead of a random variable. With historical data, sales experience and increasingly clear macro-economic conditions, coal and power enterprises are able to make a fuzzy decision – to a certain extent – when the transaction approaches. Accurate market information enables the supply chain system to satisfy the clients’ needs better, improve the profit level or avoid severe financial damages; by developing a feasible set of contracts for different parameters, it is possible to estimate whether the price difference enables supply chain coordination, requires changes or gives accounts to all involved parties of the supply chain; and without the assumption that the traditional M-V rule is unfavourable to decision makers, this thesis proposes the prospect M-V rule, which involves decision makers’ projections of future coal generation prices and enables wide applicability of the response method to contracts for differences.

In the process of building the “Belt and Road” and “Bright Road” community of interests between China and Kazakhstan, this paper proposes the construction of an inland nuclear power plant in Kazakhstan. Considering the uncertainty of investment in nuclear power generation, the authors propose the MGT (Monte-Carlo and Gaussian Radial Basis with Tensor factorization) utility evaluation model to evaluate the risk of investment in nuclear power in Kazakhstan and provide a relevant reference for decision making on inland nuclear investment in Kazakhstan.

Based on real options portfolio combined with a weighted utility function, this study takes into account the uncertainties associated with nuclear power investments through a minimum variance Monte Carlo approach, proposes a noise-enhancing process combined with geometric Brownian motion in solving complex conditions, and incorporates a measure of investment flexibility and strategic value in the investment, and then uses a deep noise reduction encoder to learn the initial values for potential features of cost and investment effectiveness. A Gaussian radial basis function used to construct a weighted utility function for each uncertainty, generate a minimization of the objective function for the tensor decomposition, and then optimize the objective loss function for the tensor decomposition, find the corresponding weights, and perform noise reduction to generalize the nonlinear problem to evaluate the effectiveness of nuclear power investment. Finally, the two dimensions of cost and risk (estimation of investment value and measurement of investment risk) are applied and simulated through actual data in Kazakhstan.

The authors assess the core indicators of Kazakhstan's nuclear power plants throughout their construction and operating cycles, based on data relating to a cluster of nuclear power plants of 10 different technologies. The authors compared it with several popular methods for evaluating the benefits of nuclear power generation and conducted subsequent sensitivity analyses of key indicators. Experimental results on the dataset show that the MGT method outperforms the other four methods and that changes in nuclear investment returns are more sensitive to changes in costs while operating cash flows from nuclear power are certainly an effective way to drive investment reform in inland nuclear power generation in Kazakhstan at current levels of investment costs.

Future research could consider exploring other excellent methods to improve the accuracy of the investment prediction further using sparseness and noise interference. Also consider collecting some expert advice and providing more appropriate specific suggestions, which will facilitate the application in practice.

The Novel Coronavirus epidemic has plunged the global economy into a deep recession, the tension between China and the US has made the energy cooperation road unusually tortuous, Kazakhstan in Central Asia has natural geographical and resource advantages, so China–Kazakhstan energy cooperation as a new era of opportunity, providing a strong guarantee for China's political and economic stability. The basic idea of building large-scale nuclear power plants in Balkhash and Aktau is put forward, considering the development strategy of building Kazakhstan into a regional international energy base. This work will be a good inspiration for the investment of nuclear generation.

This study solves the problem of increasing noise by combining Monte Carlo simulation with geometric Brownian motion under complex conditions, adds the measure of investment flexibility and strategic value, constructs the utility function of noise reduction weight based on Gaussian radial basis function and extends the nonlinear problem to the evaluation of nuclear power investment benefit.

Green energy as a transportation supply trend is irreversible. In this paper, a highway energy supply system (HESS) evolution model is proposed to provide highway transportation vehicles and service facilities with a clean electricity supply and form a new model of a source-grid-load-storage-charge synergistic highway-PV-WT integrated system (HPWIS). This paper aims to improve the flexibility index of highways and increase CO₂ emission reduction of highways.

To maximize the integration potential, a new energy-generation, storage and information-integration station is established with a dynamic master–slave game model. The flexibility index is defined to evaluate the system ability to manage random fluctuations in power generation and load levels. Moreover, CO₂ emission reduction is also quantified. Finally, the Lianhuo Expressway is taken as an example to calculate emission reduction and flexibility.

The results show that through the application of the scheduling strategy to the HPWIS, the flexibility index of the Lianhuo Expressway increased by 29.17%, promoting a corresponding decrease in CO₂ emissions.

This paper proposed a new model to capture the evolution of the HESS, which provides highway transportation vehicles and service facilities with a clean electricity supply and achieves energy transfer aided by an energy storage system, thus forming a new model of a transportation energy system with source-grid-load-storage-charge synergy. An evaluation method is proposed to improve the air quality index through the coordination of new energy generation and environmental conditions, and dynamic configuration and dispatch are achieved with the master–slave game model.

This study aims to form a new concept of power-to-gas-based virtual power plant (GVPP) and propose a low-carbon economic scheduling optimization model for GVPP considering carbon emission trading.

In view of the strong uncertainty of wind power and photovoltaic power generation in GVPP, the information gap decision theory (IGDT) is used to measure the uncertainty tolerance threshold under different expected target deviations of the decision-makers. To verify the feasibility and effectiveness of the proposed model, nine-node energy hub was selected as the simulation system.

GVPP can coordinate and optimize the output of electricity-to-gas and gas turbines according to the difference in gas and electricity prices in the electricity market and the natural gas market at different times. The IGDT method can be used to describe the impact of wind and solar uncertainty in GVPP. Carbon emission rights trading can increase the operating space of power to gas (P2G) and reduce the operating cost of GVPP.

This study considers the electrical conversion and spatio-temporal calming characteristics of P2G, integrates it with VPP into GVPP and uses the IGDT method to describe the impact of wind and solar uncertainty and then proposes a GVPP near-zero carbon random scheduling optimization model based on IGDT.

This study designed a novel structure of the GVPP integrating P2G, gas storage device into the VPP and proposed a basic near-zero carbon scheduling optimization model for GVPP under the optimization goal of minimizing operating costs. At last, this study constructed a stochastic scheduling optimization model for GVPP.

The purpose of this paper is to develop a methodology for assessing the effects of global and regional externalities that create traditional power generation industries and to propose a transition to a tariff strategy taking into account these consequences. The main purpose of the research is to analyze the current wholesale electricity tariffs in the energy market of Ukraine and propose their assessment taking into account external effects for other sectors of the economy.

At the first stage, according to observations for 2004–2019 on the amount of pollution and the cost of agricultural products in some regions of Ukraine, which is provided in 2010 prices, the impact of hazardous emissions on the cost of agricultural products was analyzed in each region. The use of panel regression allowed to combine spatial and temporal studies (12 separate areas and time interval 2004–2019). To assess the external effects of heat generation, panel regression was used, which made it possible to combine spatial and temporal data on the impact of pollution on the efficiency of agricultural production and add regional losses of agricultural business to the cost of heat generation. This paper uses optimization models to maximize the function of public utility of electricity generation, making allowances for externalities.

This research assesses the negative externalities of Ukraine's energy and confirms the need for a global transition to a low-carbon economy primarily through climate finance. The analysis revealed the presence of various influences of the factor of regional air pollution and time. The hypothesis of the existence of a negative impact of local air pollution on agricultural production has been confirmed. An increase in emissions by 1,000 tons leads to an average decrease in regional agricultural production by UAH 84 million (at the prices of 2010).

The optimization problem of the ratio of different types of generation is set on the basis of maximizing the function of social utility of electricity generation, taking into account external effects. The authors presented an optimization model of electricity generation, which corresponded to the state of the energy market for 2019, provides an opportunity to assess the contribution of the inverse external effects of each electricity sector and to estimate external tariffs for each electricity generation sector.

The purpose of this paper is to forecast wind power generation in an area through different methods, and then, recommend the most suitable one using some performance criteria.

The Box–Jenkins modeling and the Neural network modeling approaches are applied to perform forecasting for the last 12 months.

The results indicated that among the tested artificial neural network (ANN) model and its improved model, artificial neural network-genetic algorithm (ANN-GA) with RMSE of 0.4213 and R2 of 0.9212 gains the best performance in prediction of wind power generation values. Finally, a comparison between ANN-GA and ARIMA method confirmed a far superior power generation prediction performance for ARIMA with RMSE of 0.3443 and R2 of 0.9480.

Performance of the ARIMA method is evaluated in comparison to several types of ANN models including ANN, and its improved model using GA as ANN-GA and particle swarm optimization (PSO) as ANN-PSO.

The purpose of this paper is to focus on life cycle cost analysis (LCCA) of 1 MW roof-top Solar Photovoltaic (PV) panels installed in warm and humid climatic region in Southern India. The effect of actual power generated from solar PV panels on financial indicators is evaluated.

LCCA is done using the actual power generated from solar PV panels for one year. The net present value (NPV), internal rate of return (IRR), simple payback period (SPP) and discounted payback period (DPP) are determined for a base case scenario. The effect of service life and the differences between the ideal power expected and the actual power generated is evaluated.

A base case scenario is evaluated using the actual power generation data, 25-year service life and 6 percent discount rate. The NPV, IRR, SPP and DPP are found to be INR 13m, 8 percent, 10.9 years and 18.8 years respectively. It is found that the actual power generated is about one-third less than the ideal power estimated by consultants prior to project bidding. The payback period increases by 70–120 percent when the actual power generated from solar PV panels is considered.

The return on investment calculated based on ideal power generation data without considering the operation and maintenance related aspects may lead to incorrect financial assessment. Hence, strategies toward solar power generation should also focus on the actual system performance during operation.

The purpose of this paper is to estimate the locational marginal price (LMP) at each distributed generation (DG) bus based on DG unit contribution in loss reduction. This LMP value can be used by distribution company (DISCO) to control private DG owners and operate network optimally in terms of active power loss.

This paper proposes proportional nucleolus game theory (PNGT)-based iterative method to compute LMP at each DG unit. In this algorithm, PNGT has been used to identify the share of each DG unit in loss reduction. New mathematical modeling has been incorporated in the proposed algorithm to compute incentives being given to each DG owner.

The findings of this paper are that the LMP and reactive power price values for each DG unit were computed by the proposed method for the first time. Network can be operated with less loss and zero DISCO’s extra benefit, which is essential in deregulated environment. Fair competition has been maintained among private DG owners using the proposed method.

PNGT has been used for the first time for computation of LMP in distribution system based on loss reduction. Incentives to each DG unit has have been computed based on financial savings of DISCO due to loss reduction. Share of active and reactive power generation of each DG unit on change in active power loss of network due to that DG unit has been computed with new mathematical modeling. The proposed method provides LMP value to each DG unit in such a way that the network will be operated with less loss.