Dynamic simulation of the energy consumption and carbon emissions for domestic hot water production in a touristic region

Sousa, Vítor; Meireles, Inês

doi:10.1016/j.jclepro.2022.131828

Cited by 8 publications

(4 citation statements)

References 82 publications

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“…The second to fifth scenarios are simulation and comparison scenarios. The main basis for the design of these scenarios is the literature analysis in section 2, such as González, 38 Tovar Reaños et al, 40 Sousa and Meireles, 44 and the proposed five research hypotheses. For example, Scenario 4 is a simulation experiment for Energy-growth-subsidy , which also tests Hypothesis 1.…”

Section: Methodsmentioning

confidence: 99%

“…In scenario 2, the maximum energy consumption is set to a higher level (44), while the values of other variables remain unchanged. The simulation results show no significant difference between scenario 2 and the benchmark scenario in terms of Gini coefficient of energy consumption (F = 1.11, Sig (0.29)>0.05; T = 1.09, Sig (0.28)>0.05).…”

Section: Energy Consumption Adjustment and Gini Coefficientmentioning

confidence: 99%

“…It is interesting to note that different regions have different effects on energy consumption such as touristic regions. 44 Natural conditions in provinces and cities also affect energy consumption, regardless of whether energy is abundant. 32,45 In the European Union, there exists the classic economic development distinction between the core and periphery in terms of energy poverty.…”

Section: Literature Review and Hypothesesmentioning

confidence: 99%

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Energy consumption inequality in China: What can an agent-based model tell us?

Liu

2022

Energy & Environment

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China is a major energy consumer, however, related empirical research on China's unequal energy consumption is very limited. Therefore, according to the data from 30 provinces and cities, this study aims to establish a simulation model to analyse the Gini coefficient (the Lorenz curve) of China's energy consumption, and identify the main factors affecting the Gini coefficient through dynamic scenario analysis. During the simulation cycle (from 2022 to 2081), the average Gini coefficient of energy consumption fluctuates between 0.30 and 0.41, which indicates the inequality is slightly high. But the maximum value fluctuation ranges from 0.40 to 0.50, which indicates the inequality is high. The author also found that three factors—technological level, government subsidies, and growth rate of energy supply—can significantly affect the Gini coefficient of energy consumption. It is necessary to promote the technological level, increase subsidies for energy production, and promote the growth of total energy supply to significantly reduce inequity in energy consumption.

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Section: Methodsmentioning

confidence: 99%

Section: Energy Consumption Adjustment and Gini Coefficientmentioning

confidence: 99%

Section: Literature Review and Hypothesesmentioning

confidence: 99%

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Energy consumption inequality in China: What can an agent-based model tell us?

Liu

2022

Energy & Environment

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“…However, this analysis method is subjective because the actual dynamic change of parameters cannot be ignored . Some scholars have also simulated carbon emissions from urban buildings (Huo et al, 2021), land use change , and domestic hot water consumption (Sousa and Meireles, 2022) by combining static projections and dynamic simulations. However, there is a lack of analysis of carbon emission scenarios for livestock production and consumption.…”

Section: Introductionmentioning

confidence: 99%

The carbon emissions calculation, decomposition and peak scenario simulation of meat, egg, and milk in China: from a production and consumption perspective

Zhou,

Chen,

et al. 2024

Clean Techn Environ Policy

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Characterizing and quantifying livestock carbon emissions helps mitigate temperature rise and potential climate risks. Here, the spatial and temporal characteristics of China's carbon emissions of meat, egg, and milk are analyzed from the perspective of production and consumption. A driving factor decomposition model of carbon emissions of meat, egg, and milk production and consumption is established using the logarithmic mean Divisia index (LMDI). Using a static scenario analysis and dynamic Monte Carlo simulations (MCS), the evolution trajectory of carbon peak in meat, egg, and milk production and consumption in China during 2021-2030 is analyzed. China's carbon emissions from meat, egg, and milk production and consumption have risen in 2020, reaching 794.1 and 465.88 Mt, respectively. Moreover, western China has the highest carbon emissions in production (33.84%). For consumption, the carbon emissions are mainly concentrated in the eastern region (42.96%). The key drivers of carbon emissions for this production and consumption are the farmland value and residential income effects, respectively. Conversely, reverse urbanization, agricultural industry structure, and food money purchasing power effects play significant suppressive roles. Under the business-as-usual scenario (BAU), China's carbon emissions from meat, egg, and milk production and consumption in 2030 will increase by 40.1% and 4.86% from 2020, respectively. The middle low-carbon scenario (Mid-LC) mitigates carbon emission growth by 24.51% and −5.7%, respectively, for the production and consumption of meat, egg, and milk in 2030 compared to 2020. In the highly low-carbon scenario (Hig-LC), production and consumption will achieve a carbon peak by 2030. Ultimately, this study highlights the importance of reducing carbon emissions from livestock on production and consumption, providing a new perspective for green development in China and other emerging economies.

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