Community-Centric Carbon Reduction
Initiatives and Their Impact on Grid Emission
Factors: A Case Study in Kazakhstan

Kozhikov, Marat; Janelidze, Paata; Seitmukhanbet, Akbilek; Aiman, Yessekina; Karymsakov, Almas; Mkilima, Timoth

doi:10.15244/pjoes/178314

Pol. J. Environ. Stud.

2024

DOI: 10.15244/pjoes/178314

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Community-Centric Carbon Reduction Initiatives and Their Impact on Grid Emission Factors: A Case Study in Kazakhstan

Marat Kozhikov,

Paata Janelidze,

Akbilek Seitmukhanbet

et al.

Abstract: 4 The head of the greenhouse gases inventory department, Zhasyl Damu JSC 5 Department of Management and Engineering in the field of environmental protection, L.

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Cited by 2 publications

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Community Participation in Low Carbon Lifestyle Programs in Kuala Lumpur

Basri,

Sharaai,

Arsyad

et al. 2024

E-BPJ

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This paper explores community engagement in Kuala Lumpur's Low Carbon Lifestyle (LCL) programs, led by Local Agenda 21 Kuala Lumpur (LA21KL). It focuses on how community participation can advance the city's goal of achieving zero carbon emissions by 2050. Through qualitative research and in-depth interviews, the study highlights three themes: Empowering Community Participation, Strengthening Engagement in Low Carbon Programs, and Green Urban Governance. It emphasises the need for increased awareness of the benefits of LCL programs to strengthen community involvement. Strategies like developing local champions are essential for successfully implementing these initiatives.

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Community Participation in Low Carbon Lifestyle Programs in Kuala Lumpur

Basri,

Sharaai,

Arsyad

et al. 2024

E-BPJ

View full text Add to dashboard Cite

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Exploration of multiple regression analysis and prediction methods for power grid carbon emission factors

Ye,

Xiao,

Xie

et al. 2024

Applied Mathematics and Nonlinear Sciences

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Carbon emissions from the power sector account for about one-third of China’s total carbon emissions, and carbon emission reduction in the power sector is crucial to the realization of the “double carbon” goal. This paper proposes a prediction model for grid carbon emission factor based on the combination of multiple linear regression models and the GM(1,1) model. The grid carbon measurement model is built using the theory of carbon emission flow, and the grid carbon emission accounting model is built using the consumption side. The average grid carbon emission factor is calculated by dividing the grid carbon emission factor into three different dimensions. The multiple linear regression model is used to study the correlation between the independent variables and the dependent variables, and the specific values of each regression factor are predicted by combining with the GM(1,1) model, and it is judged whether the prediction model is reasonable or not. Among the contributions of the increase in carbon emissions from the power grid, the contribution of electricity consumption increased from 2010 to 2014 and reached a peak of 2.9824 million tons in 2014, and the carbon emission factor value of the power grid gradually decreased from 0.719kg CO2/(kW·h) in 2010 to 0.593kg CO2/(kW·h) in 2022. The MR-GM(1,1) model is applied to the prediction of carbon emissions from power grids, and the absolute error of the results is within 15,000 tons, and the maximum relative error is only 2.42%. The calculation and prediction of carbon emission factors of the power grid can help power grid enterprises to clarify the trend of carbon emission, which is conducive to the realization of the low-carbon goal of “carbon neutral” and “carbon peak”.

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Community-Centric Carbon Reduction Initiatives and Their Impact on Grid Emission Factors: A Case Study in Kazakhstan

Abstract: 4 The head of the greenhouse gases inventory department, Zhasyl Damu JSC 5 Department of Management and Engineering in the field of environmental protection, L.

Cited by 2 publications

References 19 publications

Community Participation in Low Carbon Lifestyle Programs in Kuala Lumpur

Community Participation in Low Carbon Lifestyle Programs in Kuala Lumpur

Exploration of multiple regression analysis and prediction methods for power grid carbon emission factors

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