Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach

This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Yu, Fu-Wing [verfasserIn] Ho, Wai-Tung [verfasserIn] Wong, Chak-Fung Jeff [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2024

Schlagwörter:	Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression

Übergeordnetes Werk:	Enthalten in: Energy for sustainable development - Oxford : Elsevier, 1994, 78
Übergeordnetes Werk:	volume:78

DOI / URN:	10.1016/j.esd.2023.101374

Katalog-ID:	ELV066747082

Internformat


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520			\|a This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.
650		4	\|a Carbon neutrality
650		4	\|a Commercial buildings
650		4	\|a Energy management
650		4	\|a Office buildings
650		4	\|a LASSO regression
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700	1		\|a Wong, Chak-Fung Jeff \|e verfasserin \|4 aut
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publishDate	2024
allfields	10.1016/j.esd.2023.101374 doi (DE-627)ELV066747082 (ELSEVIER)S0973-0826(23)00231-4 DE-627 ger DE-627 rda eng 620 VZ Yu, Fu-Wing verfasserin aut Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong. Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression Ho, Wai-Tung verfasserin aut Wong, Chak-Fung Jeff verfasserin aut Enthalten in Energy for sustainable development Oxford : Elsevier, 1994 78 Online-Ressource (DE-627)589901648 (DE-600)2473368-4 (DE-576)302178740 0973-0826 nnns volume:78 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78
spelling	10.1016/j.esd.2023.101374 doi (DE-627)ELV066747082 (ELSEVIER)S0973-0826(23)00231-4 DE-627 ger DE-627 rda eng 620 VZ Yu, Fu-Wing verfasserin aut Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong. Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression Ho, Wai-Tung verfasserin aut Wong, Chak-Fung Jeff verfasserin aut Enthalten in Energy for sustainable development Oxford : Elsevier, 1994 78 Online-Ressource (DE-627)589901648 (DE-600)2473368-4 (DE-576)302178740 0973-0826 nnns volume:78 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78
allfields_unstemmed	10.1016/j.esd.2023.101374 doi (DE-627)ELV066747082 (ELSEVIER)S0973-0826(23)00231-4 DE-627 ger DE-627 rda eng 620 VZ Yu, Fu-Wing verfasserin aut Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong. Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression Ho, Wai-Tung verfasserin aut Wong, Chak-Fung Jeff verfasserin aut Enthalten in Energy for sustainable development Oxford : Elsevier, 1994 78 Online-Ressource (DE-627)589901648 (DE-600)2473368-4 (DE-576)302178740 0973-0826 nnns volume:78 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78
allfieldsGer	10.1016/j.esd.2023.101374 doi (DE-627)ELV066747082 (ELSEVIER)S0973-0826(23)00231-4 DE-627 ger DE-627 rda eng 620 VZ Yu, Fu-Wing verfasserin aut Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong. Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression Ho, Wai-Tung verfasserin aut Wong, Chak-Fung Jeff verfasserin aut Enthalten in Energy for sustainable development Oxford : Elsevier, 1994 78 Online-Ressource (DE-627)589901648 (DE-600)2473368-4 (DE-576)302178740 0973-0826 nnns volume:78 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78
allfieldsSound	10.1016/j.esd.2023.101374 doi (DE-627)ELV066747082 (ELSEVIER)S0973-0826(23)00231-4 DE-627 ger DE-627 rda eng 620 VZ Yu, Fu-Wing verfasserin aut Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach 2024 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong. Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression Ho, Wai-Tung verfasserin aut Wong, Chak-Fung Jeff verfasserin aut Enthalten in Energy for sustainable development Oxford : Elsevier, 1994 78 Online-Ressource (DE-627)589901648 (DE-600)2473368-4 (DE-576)302178740 0973-0826 nnns volume:78 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 AR 78
language	English
source	Enthalten in Energy for sustainable development 78 volume:78
sourceStr	Enthalten in Energy for sustainable development 78 volume:78
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression
dewey-raw	620
isfreeaccess_bool	false
container_title	Energy for sustainable development
authorswithroles_txt_mv	Yu, Fu-Wing @@aut@@ Ho, Wai-Tung @@aut@@ Wong, Chak-Fung Jeff @@aut@@
publishDateDaySort_date	2024-01-01T00:00:00Z
hierarchy_top_id	589901648
dewey-sort	3620
id	ELV066747082
language_de	englisch
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author	Yu, Fu-Wing
spellingShingle	Yu, Fu-Wing ddc 620 misc Carbon neutrality misc Commercial buildings misc Energy management misc Office buildings misc LASSO regression Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach
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topic_title	620 VZ Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach Carbon neutrality Commercial buildings Energy management Office buildings LASSO regression
topic	ddc 620 misc Carbon neutrality misc Commercial buildings misc Energy management misc Office buildings misc LASSO regression
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title	Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach
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title_full	Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach
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title_sort	predicting and decarbonizing carbon emissions from building energy use in hong kong: a lasso regression approach
title_auth	Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach
abstract	This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.
abstractGer	This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.
abstract_unstemmed	This study examines the total carbon emissions of Hong Kong, which ranked 74th among 218 countries in 2021, with a focus on the contribution of building energy use. Using data from open sources at ourworldindata.org and data.gov.hk from 2001 to 2020, a Least Absolute Shrinkage and Selection Operator (LASSO) regression model is developed to predict the trajectory of carbon emissions. The significance of regression coefficients is tested using bootstrap confidence intervals, and jackknife cross-validation is employed to evaluate the model's performance. The study identifies nine significant predictors, including commercial electricity consumption, private office area, and private commercial building area, among others. The findings indicate that the current carbon reduction trend is insufficient to meet the target in 2030. To achieve carbon neutrality by 2050, the study proposes a decarbonization roadmap involving a 30–40 % reduction in commercial building electricity consumption and a decreased carbon emission factor. The study underscores the importance of strategic energy management for air conditioning systems and the promotion of green building projects in effectively decarbonizing Hong Kong.
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title_short	Predicting and decarbonizing carbon emissions from building energy use in Hong Kong: A LASSO regression approach
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