Pathways toward zero-emission textile industry by 2050: A case study of Albania

The growing energy demand in the textile and leather sub-branch presents a crucial opportunity to integrate sustainable solutions. By leveraging renewable energy systems (RES), enhancing efficiency measures, and adopting advanced technologies, the sector can address escalating energy needs while minimizing environmental impact and optimizing cost. Aligning these efforts with the National Energy and Climate Plan (NECP) and Sustainable Development Goals (SDGs) objectives strengthens supply resilience. It accelerates the transition toward a zero-emission society through sophisticated modeling tools. At the core of this research is the seamless integration of RETScreen Expert, a globally recognized tool for clean energy management, and the Low Emissions Analysis Platform (LEAP), an advanced system for energy optimization, scenario analysis, and addressing supply challenges enabling to establish a cohesive framework, effectively balancing energy demand management with bold decarbonization strategies. Artificial Intelligence (AI) further amplifies this approach, employing supervised Machine Learning (ML) techniques as AI's subset, particularly Multi Linear Regression (MLR), to forecast long-term activity levels trained on GDP and population dynamics labeled data leading to a near-perfect coefficient of determination (R²) of the association of 0.99. Simulation results highlight substantial advancements at the factory level, with annual specific energy consumption decreasing from 1255 kWh per employee to −341 kWh, effectively transforming the facility into a Positive Energy Factory (PEF). LEAP utilizes historical energy data (2005–2022), categorized by fuel type and activity level data, to generate detailed and precise projections. Transitioning away from fossil fuels, coupled with annual energy efficiency improvements of at least 1 % and integrating renewable energy systems such as photovoltaic (PV) to power electrical appliances and solar water heating (SWH) panels for hot water and steam processes, lays a clear foundation for achieving SDGs toward a Zero-Emission Textile (ZET) sub-sector by 2050. • The total decarbonization of the textile sub-branch in Albania is presented. • The LEAP model predicts the final energy demand with a growing activity level in 2050. • Fossil fuel share is replaced with energy efficiency measures to achieve a carbon-free industry. • The final energy intensity is reduced to 1% with a rise in activity level by 2050. • An avoided energy of 0.027 TWh is reached by the end of 2050.

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Publication Details

Journal
Energy Reports
Published
2025-04-04
DOI
https://doi.org/10.1016/j.egyr.2025.03.044
Citations
2
Primary Topic
Global Trade and Competitiveness
Type
article
Field-Weighted Citation Impact
4.45
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article

Pathways toward zero-emission textile industry by 2050: A case study of Albania

Blerina Kolgjini, Abdulfatai Olatunji Yakub, Aliu Olamide Oyedun, Lorenc Malka et al.
2 citations
Energy Reports
Global Trade and Competitiveness
4.45
article

Pathways toward zero-emission textile industry by 2050: A case study of Albania

Blerina Kolgjini, Abdulfatai Olatunji Yakub, Aliu Olamide Oyedun, Lorenc Malka, Abdulhameed Babatunde Owolabi, P. Malkaj, Ilda Kola
article en
2 citations

Abstract

The growing energy demand in the textile and leather sub-branch presents a crucial opportunity to integrate sustainable solutions. By leveraging renewable energy systems (RES), enhancing efficiency measures, and adopting advanced technologies, the sector can address escalating energy needs while minimizing environmental impact and optimizing cost. Aligning these efforts with the National Energy and Climate Plan (NECP) and Sustainable Development Goals (SDGs) objectives strengthens supply resilience. It accelerates the transition toward a zero-emission society through sophisticated modeling tools. At the core of this research is the seamless integration of RETScreen Expert, a globally recognized tool for clean energy management, and the Low Emissions Analysis Platform (LEAP), an advanced system for energy optimization, scenario analysis, and addressing supply challenges enabling to establish a cohesive framework, effectively balancing energy demand management with bold decarbonization strategies. Artificial Intelligence (AI) further amplifies this approach, employing supervised Machine Learning (ML) techniques as AI's subset, particularly Multi Linear Regression (MLR), to forecast long-term activity levels trained on GDP and population dynamics labeled data leading to a near-perfect coefficient of determination (R²) of the association of 0.99. Simulation results highlight substantial advancements at the factory level, with annual specific energy consumption decreasing from 1255 kWh per employee to −341 kWh, effectively transforming the facility into a Positive Energy Factory (PEF). LEAP utilizes historical energy data (2005–2022), categorized by fuel type and activity level data, to generate detailed and precise projections. Transitioning away from fossil fuels, coupled with annual energy efficiency improvements of at least 1 % and integrating renewable energy systems such as photovoltaic (PV) to power electrical appliances and solar water heating (SWH) panels for hot water and steam processes, lays a clear foundation for achieving SDGs toward a Zero-Emission Textile (ZET) sub-sector by 2050. • The total decarbonization of the textile sub-branch in Albania is presented. • The LEAP model predicts the final energy demand with a growing activity level in 2050. • Fossil fuel share is replaced with energy efficiency measures to achieve a carbon-free industry. • The final energy intensity is reduced to 1% with a rise in activity level by 2050. • An avoided energy of 0.027 TWh is reached by the end of 2050.

Energy ReportsVol. 13
Universität Hamburg (DE), Polytechnic University of Tirana (AL), Kyungpook National University (KR), Hamburg University of Technology (DE)
Decent work and economic growth
Openalex Percentile: Top 7%
Global Trade and Competitiveness
4.45
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