Actionable insights for equitable urban sustainability transition through building-level assessment of sustainable development goals – Nature

 

Report on a Building-Level Assessment Framework for the Sustainable Development Goals

1.0 Introduction: Addressing Gaps in Urban SDG Evaluation

Traditional evaluations of the Sustainable Development Goals (SDGs) are often conducted at the administrative unit level, a scale that lacks the granularity needed for effective urban policy and intervention. This research addresses this limitation by developing a data-driven framework to assess sustainability at the individual building level. The objective is to provide actionable insights for achieving equitable urban development, with a direct focus on advancing the United Nations’ 2030 Agenda for Sustainable Development.

The framework critiques and enhances concepts like the “15-minute city,” which, while promoting accessibility, can overlook barriers and exacerbate inequalities if not implemented with a focus on equity. By generating Building-Level Sustainability Scores (BLSS), this study provides a quantitative methodology to benchmark progress towards key SDGs, including:

• SDG 11 (Sustainable Cities and Communities): Making cities inclusive, safe, resilient, and sustainable.
• SDG 10 (Reduced Inequalities): Ensuring equitable access to services and opportunities for all residents.
• SDG 3 (Good Health and Well-being): Assessing access to healthcare facilities.
• SDG 1 (No Poverty): Analyzing socio-economic conditions at a granular level.
• SDG 5 (Gender Equality): Evaluating demographic distributions, although limited by data availability.

Using Hong Kong as a case study, this report details a methodology that integrates geospatial analysis and machine learning to equip urban planners and policymakers with a robust tool for data-driven decision-making, fostering urban growth that is both sustainable and equitable.

2.0 Methodology: A Data-Driven Framework for SDG Assessment

A novel algorithm was developed to assess the 17 SDGs at the building level. This process integrates extensive datasets with advanced analytical techniques to produce granular sustainability scores.

2.1 Data Collection and Integration

1. Data Acquisition: Foundational data was collected from multiple sources.
   • Building-level census data for over 40,000 residential buildings was integrated with spatial information from OpenStreetMaps.
   • Facilities data from over 100 government departments were sourced from Hong Kong’s Common Spatial Data Infrastructure (CSDI) portal.
2. Indicator Mapping: Datasets were mapped to specific SDG indicators. This involved aligning census variables (e.g., income, age, occupation) and facility locations (e.g., schools, hospitals, waste management sites) with the official targets of the 17 SDGs. This step ensures that the analysis is directly tied to the global sustainability framework.

2.2 Geospatial and Statistical Analysis

1. Accessibility Calculation: The shortest-path street distance from each building to various essential facilities was calculated using geospatial libraries. This metric is crucial for evaluating progress on SDGs related to access to services, such as SDG 3 (Health), SDG 4 (Education), and SDG 11 (Sustainable Cities).
2. Building-Level Sustainability Score (BLSS) Computation:
   • Individual indicator scores were normalized using min-max scaling to a standard 0-10 scale for comparability.
   • Scores for all indicators related to a specific SDG were aggregated to create a score for that goal (e.g., SDG 1 Score, SDG 11 Score).
   • An overall SDG score for each building was calculated by summing the scores of all 17 individual SDGs.
3. Inequality Measurement: The Gini coefficient was calculated for each SDG score across buildings, districts, and regions. This provides a quantitative measure of inequality in sustainability performance, directly addressing the core objective of SDG 10 (Reduced Inequalities).

2.3 Predictive Modeling and Scenario Simulation

1. Machine Learning Integration: A machine learning-based recommendation system was developed to suggest optimal housing based on user preferences and SDG performance. This demonstrates a practical application of the BLSS framework.
2. Scenario Analysis: The framework enables simulations to analyze the impact of new infrastructure. For example, it can model how the addition of a new childcare or elderly care facility would improve a neighborhood’s scores for SDG 3 and SDG 11, allowing for proactive and evidence-based urban planning.

3.0 Results: Multi-Scalar SDG Performance in Hong Kong

The analysis yielded detailed insights into sustainability performance at building, district, and regional levels, revealing significant spatial and demographic disparities in the attainment of the SDGs.

3.1 Building-Level and Regional Disparities

• The Kowloon region demonstrated the highest average SDG score (1.62), while Hong Kong Island had the lowest (1.38), indicating uneven progress towards SDG 11 across the city.
• Specific districts like Kowloon City achieved high scores (2.04), whereas the Islands district scored lowest (1.17), highlighting areas requiring targeted interventions to meet SDG targets.
• Analysis of top-performing regions showed that SDG 16 (Peace, Justice, and Strong Institutions), SDG 1 (No Poverty), and SDG 10 (Reduced Inequalities) were often the highest-scoring goals, reflecting relative strengths in governance and poverty alleviation efforts.

3.2 Demographic and Inequality Analysis

• Sustainability hotspots varied significantly across age groups. Younger populations (under 39) in dense urban areas of Kowloon and Hong Kong Island showed higher SDG scores, while older populations (over 65), particularly in the New Territories, faced greater disparities, pointing to challenges in achieving SDG 3 and SDG 11 for all age demographics.
• Gini coefficient analysis revealed high inequality for several environmental goals. SDG 12 (Responsible Consumption and Production), SDG 14 (Life Below Water), and SDG 15 (Life on Land) exhibited Gini values near 0.99, indicating that progress in these areas is highly inequitable.
• Significant inequality was also observed for SDG 16 among younger populations and for SDG 1 and SDG 10 among older adults, underscoring the need for age-specific policies to ensure no one is left behind.

3.3 Framework Validation and Scenario Simulation

• A paired t-test comparing the calculated city-wide SDG scores with official UN data for Hong Kong yielded a p-value of 0.146, indicating no significant statistical difference and validating the aggregation methodology.
• Scenario simulations demonstrated the framework’s utility. Modeling the introduction of an integrated elderly and childcare facility showed a significant improvement in local scores for related SDGs, providing a data-driven case for integrated urban development to meet the targets of SDG 3 and SDG 11.

4.0 Discussion and Conclusion: Towards Equitable and Actionable SDG Implementation

This research presents a significant advancement in monitoring the Sustainable Development Goals by shifting the focus from broad administrative units to the building level. This granular approach provides actionable insights that are critical for creating truly sustainable and equitable urban environments as envisioned by the 2030 Agenda.

The findings from Hong Kong reveal that while progress has been made on certain goals like SDG 16, significant spatial and demographic inequalities persist. The high Gini coefficients for environmental SDGs (12, 14, 15) and social SDGs (1, 10) highlight that the benefits of development are not being distributed evenly. This underscores the importance of applying an equity lens, central to SDG 10, to all urban planning initiatives.

The framework’s ability to conduct scenario simulations offers a powerful tool for policymakers. By modeling the impact of infrastructure projects on specific SDG indicators, cities can move from reactive to proactive planning, ensuring that investments are targeted to areas of greatest need and contribute positively to multiple sustainability objectives simultaneously. This aligns with the interconnected nature of the SDGs, where progress in one area, such as infrastructure (SDG 9), can drive improvements in health (SDG 3), education (SDG 4), and urban living (SDG 11).

In conclusion, the building-level SDG assessment methodology provides a scalable and adaptable framework for cities worldwide. By leveraging open data and advanced analytics, it empowers stakeholders to translate the global aspirations of the SDGs into concrete, localized, and equitable actions, paving the way for a more sustainable urban future for all.

Analysis of Sustainable Development Goals in the Article

1. Which SDGs are addressed or connected to the issues highlighted in the article?

The article addresses a wide range of Sustainable Development Goals (SDGs) by proposing a building-level assessment framework. The primary focus is on urban sustainability, equity, and accessibility, but the methodology links to numerous goals. The most relevant SDGs identified are:

• SDG 1: No Poverty: The article explicitly mentions assessing poverty eradication through metrics like household income and access to basic services. Figure 1 shows a high score for SDG 1 for the Sang On building, and Figure 3 notes that SDG 1 is a pivotal contributor to the overall Building-level Sustainability Scores (BLSS).
• SDG 3: Good Health and Well-being: The analysis includes access to healthcare facilities such as hospitals and elderly care centers. The scenario simulation in Figure 7 directly evaluates the impact of adding new healthcare facilities. The Gini coefficient analysis in Figure 5 also assesses inequality in health outcomes.
• SDG 4: Quality Education: The framework assesses proximity to schools and childcare centers as a key facility. Figure 1 shows a specific score for access to schools, and Figure 7 simulates the impact of adding a childcare facility.
• SDG 5: Gender Equality: The article discusses the importance of gender-specific data and analysis. It notes that while Hong Kong has an equitable gender distribution, this may not be a global trend, making gender-based scoring imperative. The methodology section (Figure 9) includes indicator 8.5.2 (Gender Pay Gap) which contributes to multiple SDGs.
• SDG 8: Decent Work and Economic Growth: The analysis incorporates economic indicators such as average individual salary, median household income, and unemployment rates (indicator 8.6.1 in Figure 10), which are central to this goal.
• SDG 9: Industry, Innovation, and Infrastructure: The core of the research is about improving urban infrastructure planning through a novel data-driven framework. Figure 2 highlights that the Kowloon City District has a perfect score of 10 for SDG 9.
• SDG 10: Reduced Inequalities: This is a central theme of the article. The research aims to identify and address inequities in accessibility and resource distribution. The use of the Gini coefficient is a direct measure of inequality for various SDG indicators across different demographic groups and regions.
• SDG 11: Sustainable Cities and Communities: This is the most prominently featured SDG. The entire research is framed around creating equitable and sustainable urban environments, referencing concepts like the “15-minute city,” affordable housing, public transportation, waste management, and access to public green spaces.
• SDG 12: Responsible Consumption and Production: The article mentions this SDG in the context of high inequality measured by the Gini coefficient (Figure 5) and includes the national recycling rate as an indicator (Figure 10).
• SDG 13: Climate Action: The introduction mentions Copenhagen’s Carbon Neutrality Plan to reduce emissions. The methodology in Figure 10 includes an indicator for national disaster risk reduction strategies (13.1.2), linking building-level resilience to climate action.
• SDG 16: Peace, Justice, and Strong Institutions: The framework assesses proximity to police stations and measures public satisfaction with services. Figure 3 notes that SDG 16 is a consistently high-scoring goal across Hong Kong, “emphasizing societal inclusivity and justice.”
• SDG 17: Partnerships for the Goals: The article mentions this SDG in the results (Figure 3) and the methodology (Figure 10), using government revenue as a parameter, which relates to domestic resource mobilization.

2. What specific targets under those SDGs can be identified based on the article’s content?

The article’s methodology and analysis, particularly in Figure 10, allow for the identification of several specific SDG targets:

1. Target 1.4: Ensure that all men and women, in particular the poor and the vulnerable, have equal rights to economic resources, as well as access to basic services. This is addressed by measuring access to facilities and services at the building level.
2. Target 3.8: Achieve universal health coverage, including financial risk protection, access to quality essential health-care services and access to safe, effective, quality and affordable essential medicines and vaccines for all. This is linked to the assessment of proximity to hospitals and elderly care facilities.
3. Target 4.1: Ensure that all girls and boys complete free, equitable and quality primary and secondary education. This is measured through access to schools.
4. Target 4.a: Build and upgrade education facilities that are child, disability and gender sensitive and provide safe, non-violent, inclusive and effective learning environments for all. This is connected to the scenario analysis of adding childcare facilities.
5. Target 8.5: Achieve full and productive employment and decent work for all women and men, including for young people and persons with disabilities, and equal pay for work of equal value. This is addressed through indicators like unemployment rate and gender pay gap.
6. Target 10.2: By 2030, empower and promote the social, economic and political inclusion of all, irrespective of age, sex, disability, race, ethnicity, origin, religion or economic or other status. This is the core of the equity analysis, using Gini coefficients to measure disparities in access for different age groups.
7. Target 11.1: By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums. The article discusses housing affordability and uses building-level census data.
8. Target 11.2: By 2030, provide access to safe, affordable, accessible and sustainable transport systems for all. This is mentioned in the introduction and is a key component of the “15-minute city” concept.
9. Target 11.6: By 2030, reduce the adverse per capita environmental impact of cities, including by paying special attention to air quality and municipal and other waste management. The framework assesses access to waste management facilities.
10. Target 11.7: By 2030, provide universal access to safe, inclusive and accessible, green and public spaces. This is mentioned as a critical infrastructure need in the introduction.
11. Target 16.6: Develop effective, accountable and transparent institutions at all levels. This is measured by the indicator “satisfaction with public services.”

3. Are there any indicators mentioned or implied in the article that can be used to measure progress towards the identified targets?

Yes, the article is built upon a framework of quantitative indicators used to calculate the Building-level Sustainability Scores (BLSS). These indicators are both explicitly stated and implied:

• Quantitative Statistical Measures:
  • Gini Coefficient: Used extensively to measure inequality in SDG progress across different age groups and regions for specific goals like SDG 1, SDG 10, and SDG 16.
  • Building-level Sustainability Score (BLSS): The primary composite indicator developed by the research to provide an overall sustainability score for each building, which is then aggregated for districts, regions, and the city.
  • Paired t-test: A statistical method used to validate the calculated scores against actual scores from official UN data, ensuring the robustness of the methodology.
• Socio-Economic Indicators:
  • Household and Individual Income: Used to assess SDG 1 (No Poverty) and SDG 8 (Decent Work). Figure 1 provides the average individual monthly salary and median household income for the Sang On building.
  • Unemployment Rate: Mentioned as a parameter for indicator 8.6.1.
  • Gender Pay Gap: Mentioned as a parameter for indicator 8.5.2.
  • Demographic Data: Age and gender distribution are used to stratify the analysis and identify disparities, particularly for SDG 5 and SDG 10.
• Accessibility and Proximity Indicators:
  • Shortest-path distance: A key geospatial metric calculated to measure the travel distance from a building to various essential facilities.
  • Access to Facilities: The framework generates specific scores for access to hospitals, elderly care, childcare, schools, police stations, and waste management facilities.
  • Access to Public Transport: A key indicator for SDG 11.2 and the 15-minute city concept.
  • Access to Public Space: An indicator for SDG 11.7.
• Environmental and Institutional Indicators:
  • National Recycling Rate: Used as a parameter for SDG 12 (indicator 12.5.1).
  • Satisfaction with Public Services: Used as a parameter for SDG 16 (indicator 16.6.2).
  • Population feeling safe walking alone: A parameter for SDG 16 (indicator 16.1.4).

4. Table of SDGs, Targets, and Indicators

SDGs	Targets	Indicators Identified in the Article
SDG 1: No Poverty	1.2: Reduce at least by half the proportion of men, women and children of all ages living in poverty. 1.4: Ensure access to basic services.	Indicator 1.2.1: Proportion of population living below the national poverty line. Indicator 1.4.1: Proportion of population living in households with access to basic services. Parameters: Household income, access to facilities.
SDG 3: Good Health and Well-being	3.8: Achieve universal health coverage.	Indicator 3.8.1: Coverage of essential health services. Indicator 3.b.3: Proportion of health facilities that have a core set of relevant essential medicines available. Parameters: Shortest-path distance to hospitals and elderly care facilities.
SDG 4: Quality Education	4.1: Ensure all children complete free, equitable and quality education. 4.a: Build and upgrade education facilities.	Indicator 4.1.1: Proportion of children and young people achieving a minimum proficiency level in reading and mathematics. Indicator 4.a.1: Proportion of schools with access to basic services. Parameters: Shortest-path distance to schools and childcare centers, education levels from census data.
SDG 5: Gender Equality	5.5: Ensure women’s full and effective participation and equal opportunities for leadership.	Indicator 5.5.2: Proportion of women in managerial positions. Parameters: Gender distribution, need for gender-specific data collection.
SDG 8: Decent Work and Economic Growth	8.5: Achieve full and productive employment and decent work for all. 8.6: Reduce the proportion of youth not in employment, education or training.	Indicator 8.5.2: Unemployment rate, by sex, age and persons with disabilities. Indicator 8.6.1: Proportion of youth (aged 15-24 years) not in employment, education or training. Parameters: Average salary, household income, unemployment rate.
SDG 10: Reduced Inequalities	10.2: Empower and promote the social, economic and political inclusion of all.	Indicator 10.2.1: Proportion of people living below 50 per cent of median income. Parameters: Gini coefficient calculated for SDG scores across age groups and regions.
SDG 11: Sustainable Cities and Communities	11.1: Access for all to adequate, safe and affordable housing. 11.2: Access to safe, affordable, accessible and sustainable transport systems. 11.6: Reduce the adverse per capita environmental impact of cities. 11.7: Universal access to safe, inclusive and accessible, green and public spaces.	Indicator 11.1.1: Proportion of urban population living in slums, informal settlements or inadequate housing. Indicator 11.2.1: Proportion of population that has convenient access to public transport. Indicator 11.6.2: Annual mean levels of fine particulate matter (e.g., PM2.5) in cities. Indicator 11.7.1: Average share of the built-up area of cities that is open space for public use. Parameters: Housing affordability, access to public transport, access to waste management, access to green space.
SDG 16: Peace, Justice and Strong Institutions	16.1: Significantly reduce all forms of violence. 16.6: Develop effective, accountable and transparent institutions. 16.7: Ensure responsive, inclusive, participatory and representative decision-making.	Indicator 16.1.4: Proportion of population that feel safe walking alone around the area where they live. Indicator 16.6.2: Proportion of population satisfied with their last experience of public services. Parameters: Proximity to police stations, satisfaction with public services.

Source: nature.com