Analyzing the impact of climate change on energy-economy-carbon nexus system in China

Han, Mei; Li, Y.P.; Suo, C.; Ma, Yuan; Lv, J.

doi:10.1016/j.apenergy.2020.114568

Cited by 85 publications

(28 citation statements)

References 34 publications

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“…The energy issue should be addressed as a key challenge to fight climate change and its adverse effects, owing to the high impact of energy consumption and production on collective emissions of greenhouse gases (GHG), due to the high dependence on fossil fuels in current production and consumption patterns. The efficient use of energy together with the consumption of clean energies are essential to address global warming [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

“…It also seeks an inclusive and just transition to a sustainable, green, and circular economy, transforming waste from the production system into valuable resources [5,6]. This plan focuses on the maintenance and protection of biodiversity and environmental sustainability through innovation in industries, enhancement of renewable energies and energy efficiency, and creation of more planet-friendly technologies together with economical and ecological transport [1][2][3][4].…”

Section: Introductionmentioning

confidence: 99%

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The Effect of Environmental Degradation, Climate Change, and the European Green Deal Tools on Life Satisfaction

2021

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Environmental issues, sustainability, and climate change have become the targets of many policies from international and governmental organizations including the EU’s European Green Deal action plan. This plan provides tools to address them—which include support for the circular economy, implementation of energy and environmental taxes, and investment in environmental protection—with the aim of favoring the well-being of EU citizens. In this context, this study analyses the impact of several parameters (environmental matters, global warming, circular economy, energy and environmental taxes, and expenditure and investment on the environment) on life satisfaction, or subjective well-being, and their effects on 33 European countries. For this purpose, four econometric models were carried out using data collected from the World Values Survey (WVS), the European Values Study (EVS), and the World Bank and Eurostat, reflecting subjective citizen satisfaction data. The findings present a highly significant inverse relationship between life satisfaction variables and both environmental problems and energy taxes. Additionally, there is a highly significant positive relationship between circular economy, environmental tax, and environmental protection expenditure parameters and life satisfaction variables, as well as, to a lesser degree of significance, with noise pollution from neighbours.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Effect of Environmental Degradation, Climate Change, and the European Green Deal Tools on Life Satisfaction

2021

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“…Degree‐days are characterized as the aggregate of the temperature differences between the outdoor temperature and a base temperature. The outdoor temperature at which there is no requirement to operate cooling or heating appliances for end‐user's comfort attainment is taken as reference, base, or balance point temperature 67 . ACs needs to operate in cooling or heating mode when the outdoor temperature is above or below the base temperature, respectively.…”

Section: Estimation Of Acs Demand Based On Cdds and Hddsmentioning

confidence: 99%

“…The outdoor temperature at which there is no requirement to operate cooling or heating appliances for end-user's comfort attainment is taken as reference, base, or balance point temperature. 67 ACs needs to operate in cooling or heating mode when the outdoor temperature is above or below the base temperature, respectively. CDDs are an indication of heat stress calculated from temperatures above a base temperature.…”

Section: Estimation Of Acs Demand Based On Cdds and Hddsmentioning

confidence: 99%

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Waseem

Liu

Zhang

et al. 2021

Int Trans Electr Energ Syst

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With the grid's evolution, the end-users demand becomes more vital for demand side management (DSM). Accurate load forecasting (LF) is critical for power system planning and using advanced demand response (DR) strategies.To design efficient and precise LF, information about various factors that influence end-users demand is required. In this paper, the impact of different factors on electrical demand and capacity of climatic factors existence and their variation is discussed and analysed. The Pearson correlation coefficient (PCC) is utilized to express the degree of electric demand correlation with metrological and calendar factors. Then, the optimal-Bayesian regularization algorithm (BRA) based on ANN for LF is presented. The effect of the number of neurons in hidden layers on output is observed to select the most appropriate option. Additionally, heating degree days (HDDs) and cooling degree days (CDDs) indices are investigated to consider the impact of air conditioners' (ACs) loads in different seasons. Case studies on data from Dallas, Texas, USA, are used to

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“…less than 10 kW) [12], followed by a sudden plummet in 2016 due to a dramatic cut to feed-in tariff for solar, announced by the UK government [13]. In addition, future electricity demand has been shown to be significantly affected by future temperature and economy, both are highly uncertain [14]. Finally, even though smart grid technologies can increase system flexibility to accommodate intermittent renewable energy, the potential of smart grid technologies heavily depends on public's acceptance, which is influenced by perceived ease of use and risk, and is thus uncertain [15].…”

Section: Introductionmentioning

confidence: 99%

Using clustering algorithms to characterise uncertain long-term decarbonisation pathways

Pye

Keppo

2020

Applied Energy

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Long-term decarbonisation pathways to achieve ambitious low-carbon targets involve a range of uncertainties. Different energy system modelling approaches can be used to systematically evaluate the influence of the uncertainties, but this often leads to an unmanageable number of pathways. Summarising the large ensemble through a more limited number of representative pathways, to inform stakeholders, can be challenging. This study thus explores how to identify representative decarbonisation pathways using clustering algorithms, which can assist in grouping similar data points in uncategorised datasets, such as pathway ensembles. However, the suitability of clustering algorithms for pathway characterisation has not been investigated to date. Hence, k-means, hierarchical clustering, Gaussian mixture model, spectral clustering, and density-based clustering are adopted for comparisons. An illustrative pathway ensemble for the United Kingdom is applied to evaluate their performance based on cluster validity indices. Three metric transformations, including power, standardisation and sectoral standardisation, are also applied to create three additional sets of pathways for testing. The k-means algorithm is found to outperform others consistently, although hierarchical clustering might also be applicable if the distribution of pathway proximity is uneven. The results also highlight the utility of the approach in revealing distinctive trade-offs between technologies among the identified representative pathways. For instance, the electrification of heating can be replaced by district heating in the residential sector. The described, novel approach can be applied to characterise other sets of pathways, with greater technological details generated by any energy system models, to reveal insights for long-term decarbonisation.

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Analyzing the impact of climate change on energy-economy-carbon nexus system in China

Cited by 85 publications

References 34 publications

The Effect of Environmental Degradation, Climate Change, and the European Green Deal Tools on Life Satisfaction

The Effect of Environmental Degradation, Climate Change, and the European Green Deal Tools on Life Satisfaction

Optimal BRA based electric demand prediction strategy considering instance‐based learning of the forecast factors

Using clustering algorithms to characterise uncertain long-term decarbonisation pathways

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