Search results

The purpose of this paper is to examine the effectiveness of an improved dummy variables control grey model (DVCGM) considering the hysteresis effect of government policies in China's energy intensity (EI) forecasting.

Energy consumption is considered as an important driver of economic development. China has introduced policies those aim at the optimization of energy structure and EI. In this study, EI is forecasted by an improved DVCGM, considering the hysteresis effect of energy-saving policies of the government. A nonlinear optimization method based on particle swarm optimization (PSO) algorithm is constructed to calculate the hysteresis parameter. A one-step rolling mechanism is applied to provide input data of the prediction model. Grey model (GM) (1, N), DVCGM (1, N) and ARIMA model are applied to test the accuracy of the improved DVCGM (1, N) model prediction.

The results show that the improved DVCGM provides reliable results and works well in simulation and predictions using multivariable data in small sample size and time-lag virtual variable. Accordingly, the improved DVCGM notes the hysteresis effect of government policies and significantly improves the prediction accuracy of China's EI than the other three models.

This study estimates the EI considering the hysteresis effect of energy-saving policies in China by using an improved DVCGM. The main contribution of this paper is to propose a model to estimate EI, considering the hysteresis effect of energy-saving policies and improve forecasting accuracy.

To achieve sustainable development in shipping, accurately identifying the impact of artificial intelligence on shipping carbon emissions and predicting these emissions is of utmost importance.

A multivariable discrete grey prediction model (WFTDGM) based on weakening buffering operator is established. Furthermore, the optimal nonlinear parameters are determined by Grey Wolf optimization algorithm to improve the prediction performance, enhancing the model’s predictive performance. Subsequently, global data on artificial intelligence and shipping carbon emissions are employed to validate the effectiveness of our new model and chosen algorithm.

To demonstrate the applicability and robustness of the new model in predicting marine shipping carbon emissions, the new model is used to forecast global marine shipping carbon emissions. Additionally, a comparative analysis is conducted with five other models. The empirical findings indicate that the WFTDGM (1, N) model outperforms other comparative models in overall efficacy, with MAPE for both the training and test sets being less than 4%, specifically at 0.299% and 3.489% respectively. Furthermore, the out-of-sample forecasting results suggest an upward trajectory in global shipping carbon emissions over the subsequent four years. Currently, the application of artificial intelligence in mitigating shipping-related carbon emissions has not achieved the desired inhibitory impact.

This research not only deepens understanding of the mechanisms through which artificial intelligence influences shipping carbon emissions but also provides a scientific basis for developing effective emission reduction strategies in the shipping industry, thereby contributing significantly to green shipping and global carbon reduction efforts.

The multi-variable discrete grey prediction model developed in this paper effectively mitigates abnormal fluctuations in time series, serving as a valuable reference for promoting global green and low-carbon transitions and sustainable economic development. Furthermore, based on the findings of this paper, a grey prediction model with even higher predictive performance can be constructed by integrating it with other algorithms.

This study aims to predict China's carbon emission intensity and put forward a set of policy recommendations for further development of a low-carbon economy in China.

In this paper, the Interaction Effect Grey Power Model of N Variables (IEGPM(1,N)) is developed, and the Dragonfly algorithm (DA) is used to select the best power index for the model. Specific model construction methods and rigorous mathematical proofs are given. In order to verify the applicability and validity, this paper compares the model with the traditional grey model and simulates the carbon emission intensity of China from 2014 to 2021. In addition, the new model is used to predict the carbon emission intensity of China from 2022 to 2025, which can provide a reference for the 14th Five-Year Plan to develop a scientific emission reduction path.

The results show that if the Chinese government does not take effective policy measures in the future, carbon emission intensity will not achieve the set goals. The IEGPM(1,N) model also provides reliable results and works well in simulation and prediction.

The paper considers the nonlinear and interactive effect of input variables in the system's behavior and proposes an improved grey multivariable model, which fills the gap in previous studies.

The energy efficiency policy is a priority component of the overall economic policy of different countries striving to ensure the competitiveness and sustainability of national economic development. The improvement of energy efficiency represents an important economic task for the post-Soviet countries, characterized by excessive energy intensity of the economy, and the solution of this task requires proper information and analytical support: a system for accounting and analyzing energy consumption indicators. The present research is aimed at developing the tools to support decision-making in the sphere of evaluation and estimation of performance of the State energy efficiency policy of territories and testing these tools on the example of Russian regions. The study has been carried out using the methods of statistics, economic, mathematical and econometric modeling, structural, dynamic and comparative analyses. The following tools have been proposed: the method for differentiated accounting of various factors’ influence on the dynamics of energy consumption in the regions and for estimating the index of technological efficiency of electricity consumption; the method for the empirical classification of territories by types of their energy and economic development. We’ve revealed the general trend and typological features in the dynamics of electricity consumption efficiency indicators in the constituent entities of the Russian Federation and carried out the decomposition factor and comparative analysis of energy consumption patterns of the Volgograd region over 2005–2014 on the basis of the proposed tools.

The global warming, caused by the anthropogenic greenhouse gases, has been one of the major worldwide issues over the last decades. Among them, carbon dioxide (CO₂) is the most important one and is responsible for more than the two-third of the greenhouse effect. Currently, greenhouse gas emissions and CO₂ emissions – the root cause of the global warming – in particular are being examined closely in the fields of science and they also have been put on the agenda of the political leaders. The purpose of this paper is to predict the energy-related CO₂ emissions through using different discrete grey models (DGMs) in Turkey and total Europe and Eurasia region.

The proposed DGMs will be applied to predict CO₂ emissions in Turkey and total Europe and Eurasia region from 2015 to 2030 using data set between 1965 and 2014. In the first stage of the study, DGMs without rolling mechanism (RM) will be used. In the second stage, DGMs with RM are constructed where the length of the rolling horizons of the respected models is optimised.

In the first stage, estimated values show that non-homogeneous DGM is the best method to predict Turkey’s energy-related CO₂ emissions whereas DGM is the best method to predict the energy-related CO₂ emissions for total Europe and Eurasia region. According to the results in the second stage, NDGM with RM (k=26) is the best method for Turkey while optimised DGM with RM (k=4) delivers most reliable estimates for total Europe and Eurasia region.

This study illustrates the effect of different DGM approaches on the estimation performance for the Turkish energy-related CO₂ emission data.

Iran as an energy‐rich country faces many challenges in the optimal utilization of its vast resources. High rates of population and economic growth, a generous subsidies program, and poor resource management have contributed to rapidly growing energy consumption and high energy intensity over the past decades. The continuing trend of rising energy consumption will bring about new challenges as it will shrink oil export revenues, restraining economic activities. This calls for a study to explore alternative scenarios for the utilization of energy resources in Iran. The purpose of this paper is to model demand for energy in Iran and develop two business‐as‐usual and efficiency scenarios for the period 2005‐2030.

The authors use a techno‐economic or end‐use approach to model energy demand in Iran for different types of energy uses and energy carriers in all sectors of the economy and forecast it under two scenarios: business as usual (BAU) and efficiency.

Iran has a huge potential for energy savings. Specifically, under the efficiency scenario, Iran will be able to reduce its energy consumption 40 percent by 2030.The energy intensity can also be reduced by about 60 percent to a level lower than the world average today.

The paper presents a comprehensive study that models the Iranian energy demand in different sectors of the economy, using data at different aggregation levels and a techno‐economic end‐use approach to illuminate the future of energy demand under alternative scenarios.

This paper aims to study the ongoing and emerging technological changes in the global energy sector from the frequently neglected perspective of their potential destructive impact on the Russian economy.

Having reviewed existing global energy forecasts made by reputable multilateral and national government agencies, major energy corporations and specialised consulting firms, the authors noticed that most of them are by and large based on the extrapolation of conventional long-term trends depicting gradual growth of fossil fuels’ demand and catching-up supply. Unlike this approach, the paper focuses on the possible cases when conventional trends are broken, supply–demand imbalances become huge and the situation in the global energy markets is rapidly and dramatically changing with severe consequences for the Russian economy, seriously dependent on fossil fuels exports. Revealing these stress scenarios and major drivers leading to their realisation are in the focus of the research. Based on the Social, Technological, Economic, Environmental, Political, Values (analytical framework) (STEEPV) approach, the authors start from analysing various combinations of factors capable to launch stress scenarios for the Russian economy. Formulating concrete stress scenarios and assessing their negative impact on the Russian economy constitute the next step of the analysis. In conclusion, the paper underlines the urgency to integrate stress analysis related to global energy trends into the Russian national systems of technology foresight and strategic planning, which are now in the early stages of development.

The analysis of global energy market trends and various combinations of related economic, political, technological and ecological factors allowed to formulate four stress scenarios particularly painful for the Russian economy. They include the currently developing scenario “Collapse of oil prices”, and three potential ones: “Gas abundance”, “Radical de-carbonisation” and “Hydrogen economy”. One of the most important conclusions of the paper is that technology-related drivers are playing the leading role in stress scenario realisation, but it is usually a specific combination of other drivers (interlacing with technology-related factors) that could trigger the launch a particular scenario.

This study’s approach is based on the assumption that Russia’s dependence on hydrocarbons exports as one of the main structural characteristics of the Russian economy will remain intact. However, for the long-term perspective, this assumption might not hold true. So, new research will be needed to review the stress scenarios within the context of radical diversification of the Russian economy.

This paper suggests a number of practical steps aimed at introducing stress analysis as one of the key functions within the energy-related sectoral components of the Russian national systems of technology forecasting and strategic planning.

The novelty of this paper is determined both by the subject of the analysis and approach taken to reveal it. In contrast to most of research in this area, the main focus has been moved from the opportunities and potential benefits of contemporary technology-related global energy shifts to their possible negative impact on the national economy. Another important original feature of the approach is that existing global energy forecasts are used only as a background for core analysis centred around the cases when conventional energy trends are broken.

This paper aims to extend the small body of literature on energy industry transitions on firm level. A growing number of articles shed light on paradigm shifts in the energy industry and the influence of renewable energies on industry structures. In the majority of cases, the authors analyze changes on a global or national level.

Energy companies’ forecasting capabilities are particularly important to enable them to react in time to upcoming changes in industry structures. In this context, we analyze annual reports of German energy companies to evaluate their economic and technological forecasting competencies.

Big energy providers offer high economic forecasting quality, but seem to be less able to derive valid forecasts in terms of renewable energies from the currently unstable political frameworks. On the contrary, renewable energy companies do not seem to suffer from these difficulties and provide good foresting accuracy in terms of renewable energy development, but show less accurate economic forecasting quality.

Big energy providers need to find the means of responding to the challenges and integrate changing political guidelines and support into their forecasting system. Renewable energy companies, in contrast, should focus on company-level profitability and the respective economic forecasting competencies.

This paper makes a significant contribution to the literature on the subject of energy industry transitions by providing insights from publicly available data on firm level. The findings are highly relevant for managers of the energy industry and policy makers in this field.

This paper aims to clarify the relationship between foreign direct investment (FDI) and carbon intensity. This study uses the dynamic panel data model to study and provide fresh evidence for the issue.

This study first uses the dynamic panel data model to consider the endogeneity problem, and applies a system-generalized method of moments estimator to study the effect of FDI on carbon intensity using the panel data of 188 countries during 1990-2013.

The result shows that FDI has a significant negative impact on carbon intensity of the host country. After considering the other factors, including share of fossil fuels, industrial intensity, urbanization level and trade openness, the impact of FDI on carbon intensity is still significantly positive. In addition, FDI also has a significant negative impact on carbon intensity of high-income countries and middle- and low-income countries.

This paper offers two contributions to the literature on the effect of FDI on carbon intensity. From a methodological perspective, this paper is the first to apply a dynamic panel data model to study the effect of FDI on carbon intensity using worldwide panel data. Second, this paper is the first to analyze the effect of FDI on carbon intensity in different countries with different income levels separately.

Road freight carbon dioxide (CO₂) emissions are determined by a complex interaction between shippers and hauliers within the boundaries set by regulations and economic factors. It is necessary to gain understanding about the various driving forces and trends affecting these to promote low carbon future. The purpose of this paper is to find out what factors affect the long-term future development of road freight CO₂ emissions and whether the long-term emission targets will be achieved.

An international comparison of similar Delphi surveys is carried out in Finland, Norway, and Sweden.

The Delphi surveys indicate that the structural change of the economy, changes of consumer habits, concerns of energy and environment and changes in logistics practices and technology are the overarching trends shaping the future of the energy efficiency and CO₂ emissions of road freight transport. The expert forecasts for Finland and Sweden highlight that reaching the carbon emission target of 30 per cent reduction for the year 2030 is possible. However, the CO₂ emissions may also increase significantly even though the CO₂ intensity would decrease, as the Norwegian forecast shows.

This study combined quantitative and qualitative analysis. The results confirmed that similar factors are seen to affect the future in all three countries, but with some national differences in the likely effects of the factors. Future research using the same methodology would enable wider analysis of the global significance of these driving forces.