Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Transfer: Per Capita data was reported at 0.397 Unit in 2021. This records a decrease from the previous number of 0.420 Unit for 2016. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Transfer: Per Capita data is updated yearly, averaging 0.420 Unit from Dec 1996 (Median) to 2021, with 6 observations. The data reached an all-time high of 0.427 Unit in 2006 and a record low of 0.397 Unit in 2021. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax & Post-Social Transfer: Per Capita data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient. Data prior to 2011 excludes Government’s one-off relief measures.

Hong Kong SAR (China) Gini Coefficient: Monthly Household Income (MHI): Original data was reported at 0.539 Unit in 2016. This records an increase from the previous number of 0.537 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: Monthly Household Income (MHI): Original data is updated yearly, averaging 0.497 Unit from Dec 1971 (Median) to 2016, with 10 observations. The data reached an all-time high of 0.539 Unit in 2016 and a record low of 0.430 Unit in 1976. Hong Kong SAR (China) Gini Coefficient: Monthly Household Income (MHI): Original data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

Historical dataset showing Hong Kong income inequality - gini coefficient by year from N/A to N/A.

Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax: Per Capita data was reported at 0.482 Unit in 2016. This records a decrease from the previous number of 0.490 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax: Per Capita data is updated yearly, averaging 0.482 Unit from Dec 1996 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.490 Unit in 2011 and a record low of 0.478 Unit in 2001. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax: Per Capita data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

Hong Kong Population - Table E305 : Gini Coefficient by household size, 2006, 2011 and 2016

Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax data was reported at 0.524 Unit in 2016. This records an increase from the previous number of 0.521 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax data is updated yearly, averaging 0.521 Unit from Dec 1996 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.524 Unit in 2016 and a record low of 0.508 Unit in 1996. Hong Kong SAR (China) Gini Coefficient: MHI: Post-Tax data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

1. Global_1028_cities_Gini_trend.shpThis data represents the annual dynamic and trends of the Gini index for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings. Key attributes in the shapefile data:(1) Trend: temporal trend of Gini index;(2) P: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.2. Global_1028_cities_Gini_thres20_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.20) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_20: temporal trend of Gini index;(2) P_20: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.3. Global_1028_cities_Gini_thres30_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.30) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_30: temporal trend of Gini index;(2) P_30: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.4. Global_1028_cities_Gini_thres40_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.40) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_40: temporal trend of Gini index;(2) P_40: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.5. Global_1028_cities_Gini_thres50_trend.shpThis data represents the annual dynamic and trends of the Gini index (threshold = 0.50) for global 1028 cities in 2000-2018 using the WorldPop population and Landsat-derived greenspace mappings, together with threshold-based linear unmixing classification approach Key attributes in the shapefile data:(1) Trend_50: temporal trend of Gini index;(2) P_50: P-value at a statistically significant level of 0.05;(3) Y2000-Y2018: Annual Gini index for year 2000 to 2018;(4) SouthNorth: Categories of Global South or Global North;(5) Continent: Categories of continents;(6) lat/long: Latitude/longitude of the city centriod;(7) urbanArea: urban areas in Km2.

Hong Kong SAR (China) Gini Coefficient: MHI: Original: Per Capita data was reported at 0.499 Unit in 2016. This records a decrease from the previous number of 0.507 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Per Capita data is updated yearly, averaging 0.499 Unit from Dec 1996 (Median) to 2016, with 5 observations. The data reached an all-time high of 0.507 Unit in 2011 and a record low of 0.491 Unit in 2001. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Per Capita data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

Chine, RAS de Hong Kong: Gini income inequality index: Pour cet indicateur, La Banque mondiale fournit des données pour la Chine, RAS de Hong Kong de à . La valeur moyenne pour Chine, RAS de Hong Kong pendant cette période était de index points avec un minimum de index points en et un maximum de index points en .

Hong Kong SAR (China) Gini Coefficient: MHI: Original: Economically Active Household (EAH) data was reported at 0.482 Unit in 2016. This records a decrease from the previous number of 0.489 Unit for 2011. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Economically Active Household (EAH) data is updated yearly, averaging 0.489 Unit from Dec 2006 (Median) to 2016, with 3 observations. The data reached an all-time high of 0.490 Unit in 2006 and a record low of 0.482 Unit in 2016. Hong Kong SAR (China) Gini Coefficient: MHI: Original: Economically Active Household (EAH) data remains active status in CEIC and is reported by Census and Statistics Department. The data is categorized under Global Database’s Hong Kong SAR (China) – Table HK.G130: Gini Coefficient.

1. Global_1028_cities_urban_boundary.shp This data represents the continuous built-up areas for the 1028 cities selected in this study.

2. Global_1028_cities_GE_Gini.shp This data represents the greenspace exposre and inequality assessment for global 1028 cities using the fine-resolution population and greenspace mappings. Key attributes in the shapefile data: (1) GExpo100m: Greenspace exposure assessment using 100-m grid without considering nearby buffer zones; (2) GExpo500m: Greenspace exposure assessment using a 500-m buffer disance; (3) GExpo1km: Greenspace exposure assessment using a 1000-m buffer disance; (4) GExpo1500m: Greenspace exposure assessment using a 1500-m buffer disance; (5) GExpo500mG: Gini index of greenspace exposure assessment using 500-m buffer distance; (6) SouthNorth: Categories of Global South or Global North; (7) Continent: Categories of continents; (8) lat/long: Latitude/longitude of the city centriod; (9) urbanArea: urban areas in Km2.

3. Global_1028_cities_GE_Gini_four_seasons.shp This data represents the greenspace exposure and inequality assessment for global 1028 cities over four seasons (i.e., Spring, Summer, Fall, and Winter). Key attributes in the shapefile data: (1) GE_Spring: Greenspace exposure assessment in Spring using a 500-m buffer disance; (2) GE_Summer: Greenspace exposure assessment in Summer using a 500-m buffer disance; (3) GE_Fall: Greenspace exposure assessment in Fall using a 500-m buffer disance; (4) GE_Winter: Greenspace exposure assessment in Winter using a 500-m buffer disance; (5) Gini_Sprin: Greenspace exposure inequality assessment in Spring using a 500-m buffer disance; (6) Gini_Summe: Greenspace exposure inequality assessment in Summer using a 500-m buffer disance; (7) Gini_Fall: Greenspace exposure inequality assessment in Fall using a 500-m buffer disance; (8) Gini_Winte: Greenspace exposure inequality assessment in Winter using a 500-m buffer disance; (9) Gini_Dif_W: Greenspace exposure inequality difference between Summer and Winter using a 500-m buffer disance.

As a critical engine for national economic growth, mega urban agglomerations have significant scale effects and economic and environmental spillover effects. This paper aims to study the green and low-carbon coordinated development of mega urban agglomerations to evaluate the country’s level of ecological civilization and its green and low-carbon development. The traditional research on green and low-carbon urban development tends to homogenize the redistribution theme, leading to significant errors in spatial allocation. This results in a lower accuracy of spatial distribution calculations for green development. Additionally, the research is constrained by data precision and methodology, making it challenging to measure the spatial differences in green and low-carbon development within urban clusters at the block level. This limitation hinders the ability to conduct detailed studies on the efficiency variations of green and low-carbon development in urban clusters. To achieve this aim, the study adopts the DPSR framework of the economic, resource, social, and ecological environment complex system and focuses on the Guangdong-Hong Kong-Macao Greater Bay Area in China. The study uses the entropy method, Gini coefficient method, and non-expected output super-efficiency SBM model to analyze the spatial effects and development efficiency of green and low-carbon development in this region from 2006 to 2020. The study results indicate that: (1) the overall level of green and low-carbon development in the Greater Bay Area is on the rise, with Shenzhen, Guangzhou, Foshan, and Zhuhai showing more stable development than other cities. Foreign direct investment and fixed asset investment in science and technology have significantly promoted green and low-carbon development. (2) The spatial differences in the region’s level of green and low-carbon development have narrowed trends, mainly due to differences between regions. However, well-developed cities such as Guangzhou and Shenzhen have taken the initiative to lead the development of other cities, fully leveraging their advantages in science and technology, geographical location, and other resources to promote the improvement of the external orientation of other cities. (3) The overall development efficiency of green and low-carbon in the Greater Bay Area is on the rise, with Guangzhou region showing overall stability, and Shenzhen region and Zhuhai region experiencing multiple ups and downs in their development. The three sub-regions show significant differences, but the balance and coordination of development have significantly improved. Finally, this study provides theoretical support for the future green and low-carbon development of urban clusters. It is advantageous for integrating the mainstream policy analysis framework of environmental economics with the complex adaptive systems of urban clusters. The research expands the boundaries of existing theoretical studies and offers new methodological approaches for interdisciplinary research. The study achieves a balance between the opportunity effects of green and low-carbon development and environmental policy constraints in super large urban clusters, effectively enhancing resource utilization efficiency in these clusters.

基尼系数：MHI：原本：人均：EAH在12-01-2016达0.474单位，相较于12-01-2011的0.485单位有所下降。基尼系数：MHI：原本：人均：EAH数据按年更新，12-01-2006至12-01-2016期间平均值为0.484单位，共3份观测结果。该数据的历史最高值出现于12-01-2011，达0.485单位，而历史最低值则出现于12-01-2016，为0.474单位。CEIC提供的基尼系数：MHI：原本：人均：EAH数据处于定期更新的状态，数据来源于政府统计处，数据归类于全球数据库的中国香港特别行政区 – Table HK.G130: Gini Coefficient。

基尼系数：MHI：税后和福利转移后：人均：EAH在12-01-2021达0.376单位，相较于12-01-2016的0.402单位有所下降。基尼系数：MHI：税后和福利转移后：人均：EAH数据按年更新，12-01-2006至12-01-2021期间平均值为0.401单位，共4份观测结果。该数据的历史最高值出现于12-01-2006，达0.412单位，而历史最低值则出现于12-01-2021，为0.376单位。CEIC提供的基尼系数：MHI：税后和福利转移后：人均：EAH数据处于定期更新的状态，数据来源于政府统计处，数据归类于全球数据库的中国香港特别行政区 – Table HK.G130: Gini Coefficient。

基尼系数：MHI：税后和福利转移后：EAH在12-01-2016达0.422单位，相较于12-01-2011的0.430单位有所下降。基尼系数：MHI：税后和福利转移后：EAH数据按年更新，12-01-2006至12-01-2016期间平均值为0.430单位，共3份观测结果。该数据的历史最高值出现于12-01-2006，达0.436单位，而历史最低值则出现于12-01-2016，为0.422单位。CEIC提供的基尼系数：MHI：税后和福利转移后：EAH数据处于定期更新的状态，数据来源于政府统计处，数据归类于全球数据库的中国香港特别行政区 – Table HK.G130: Gini Coefficient。

基尼系数：MHI：原本：经济活动家庭（EAH）在12-01-2016达0.482单位，相较于12-01-2011的0.489单位有所下降。基尼系数：MHI：原本：经济活动家庭（EAH）数据按年更新，12-01-2006至12-01-2016期间平均值为0.489单位，共3份观测结果。该数据的历史最高值出现于12-01-2006，达0.490单位，而历史最低值则出现于12-01-2016，为0.482单位。CEIC提供的基尼系数：MHI：原本：经济活动家庭（EAH）数据处于定期更新的状态，数据来源于政府统计处，数据归类于全球数据库的中国香港特别行政区 – Table HK.G130: Gini Coefficient。

基尼系数：MHI：税后在12-01-2016达0.524单位，相较于12-01-2011的0.521单位有所增长。基尼系数：MHI：税后数据按年更新，12-01-1996至12-01-2016期间平均值为0.521单位，共5份观测结果。该数据的历史最高值出现于12-01-2016，达0.524单位，而历史最低值则出现于12-01-1996，为0.508单位。CEIC提供的基尼系数：MHI：税后数据处于定期更新的状态，数据来源于政府统计处，数据归类于全球数据库的中国香港特别行政区 – Table HK.G130: Gini Coefficient。

基尼系数：家庭月收入（MHI）：原本在12-01-2016达0.539单位，相较于12-01-2011的0.537单位有所增长。基尼系数：家庭月收入（MHI）：原本数据按年更新，12-01-1971至12-01-2016期间平均值为0.497单位，共10份观测结果。该数据的历史最高值出现于12-01-2016，达0.539单位，而历史最低值则出现于12-01-1976，为0.430单位。CEIC提供的基尼系数：家庭月收入（MHI）：原本数据处于定期更新的状态，数据来源于政府统计处，数据归类于全球数据库的中国香港特别行政区 – Table HK.G130: Gini Coefficient。