United Nations Sustainable Development Goals Research Articles

Heritage as a development engine for people in nature: A case study of Wulingyuan Scenic and Historic Interest Area, China

This paper explores strategies to promote sustainable tourism for people living in natural areas through a case study implemented in the renowned World Natural Heritage Wulingyuan Scenic and Historic Interest Area, a World Heritage Site inscribed for its natural values. The study is part of the UNESCO World Heritage and Sustainable Tourism Program’s Chinese Pilot Studies. Various interventions, including understanding local values, revitalizing traditional handicrafts, and making culinary innovations, were implemented to enrich tourist experiences, empower local communities, and boost economic opportunities. Based on these interventions, this paper discusses community capacity building, stakeholder engagement, and conflict resolution as initiatives to enhance heritage conservation and promote sustainable tourism at the community level. On the level of the World Heritage property, a new management zone, the Traditional Eco-agricultural Heritage Zone, recognized the residency of the locals within the heritage site and fostered the harmonious co-existence of culture and nature. The successful integration of heritage conservation, community development, and sustainable tourism in Wulingyuan not only ensures the conservation of the site but also contributes to United Nations Sustainable Development Goals. This case study offers a model for sustainable development of Natural World Heritage Sites, emphasizing the importance of respecting and involving diverse local communities.

Retrieval of chromium and mercury concentrations in agricultural soils: Using spectral information, environmental covariates, or a fusion of both?

The universal contamination of arable land with potentially toxic elements (PTEs) poses a severe threat to food security and jeopardizes worldwide efforts to meet the United Nations Sustainable Development Goals (SDGs). How to obtain more reliable concentrations of PTEs in regional agricultural soils is a priority problem to be solved. Multispectral satellite remote sensing, with its advantages of high spatial and temporal resolution, broad coverage, and low cost, offers the potential to acquire spatial distribution of PTEs in agricultural soils over large areas. However, owing to the complexity of soil environments and the insufficiency of spectral information, the mechanism for retrieving concentrations of PTEs in agricultural soils via multispectral satellites is not yet clear, and the accuracy needs to be improved. In this study, we aimed to assess whether employing a fusion of spectral information and environmental covariates, results in more accurate retrievals of PTEs, specifically chromium (Cr) and mercury (Hg), in croplands than does employing spectral information alone. Three machine learning algorithms—kernel-based support vector machine (SVM), neural network-based back propagation neural network (BPNN), and tree-based extreme gradient boosting (XGBoost)—were developed to retrieve Cr and Hg concentrations in agricultural soils. The results showed that the fusion of spectral information and environmental covariates combined with the XGBoost model performed best in retrieving both Cr and Hg concentrations in agricultural soils with coefficient of determination (R2) values of 0.73 and 0.74, respectively. Environmental covariates were important variables for determining Cr and Hg concentrations in agricultural soils, but the ability to retrieve these element concentrations by utilizing spectral information alone was limited. High Cr concentrations occurred in central towns and southern hilly mountains. High Hg concentrations were detected in the northwestern region and southern hilly mountains. The potential of fusing spectral information and environmental covariates to precisely retrieve PTE concentrations in agricultural soils can serve as a reference for agricultural soil health information monitoring worldwide.

The United Nations Sustainable Development Goal 13 Climate Action and efforts of UK banks towards a low carbon economy?

The United Nations Sustainable Development Goal 13 Climate Action and efforts of UK banks towards a low carbon economy?

Naturalistic Decision-Making in Intentional Communities: Insights from Youth, Disabled Persons, and Children on Achieving United Nations Sustainable Development Goals for Equality, Peace, and Justice

The seventeen UN SDGs address critical global challenges. Among them, Goal 10—reducing inequality—and Goal 16—promoting peace, justice, and strong institutions—serve as foundational pillars in democracies, enabling the achievement of all other goals. Children, youth, and persons with disabilities are among those who stand to benefit most from these goals. Insights from the naturalistic decision-making practices of intentional communities, often framed as Contenders or Deviants in social construction theory, could be instrumental in advancing these objectives. This study examines the decision-making practices of three intentional communities representing youth, disabled persons, and children, each fostering a different version of equitable, peaceful, and justice-oriented governance to build strong institutions. The communities studied include a self-producing Korean popular music (K-pop) group representing youth Contenders, a mental health-supporting annual English conference for individuals on the autistic spectrum, and a Canadian alternative education, self-directed public senior elementary and secondary school—both considered Deviant societies in social construction theory, one focusing on disabled persons and the other on children. The historical method assesses the effectiveness of these communities’ preferred practices in achieving Goals 10 and 16. The results offer actionable insights for enhancing equality, peace, and justice while strengthening institutions to realize the full range of UN SDGs in democratic societies.

Nonsense-Mediated mRNA Decay in Human Health and Diseases: Current Understanding, Regulatory Mechanisms and Future Perspectives.

Nonsense-mediated mRNA decay (NMD) is a surveillance mechanism that is conserved across all eukaryotes ensuring the quality of transcripts by targeting messenger RNA (mRNA) harbouring premature stop codons. It regulates the gene expression by targeting aberrant mRNA carrying pre-termination codons (PTCs) and eliminates C-terminal truncated proteins. NMD distinguishes aberrant and non-aberrant transcript by looking after long 3' UTRs and exon-junction complex (EJC) downstream of stop codon that indicate the presence of PTC. Therefore, NMD modulates cellular surveillance and eliminates the truncated proteins but if the PTC escapes the surveillance pathway it can lead to potential negative phenotype resulting in genetic diseases. The alternative splicing also contributes in formation of NMD-sensitive isoforms by introducing PTC. NMD plays a complex role in cancer, it can either aggravate or downregulates the tumour. Some tumours agitate NMD to deteriorate mRNAs encoding tumour suppressor proteins, stress response proteins and neoantigens. In other case, tumours suppress the NMD to encourage the expression of oncoproteins for tumour growth and survival. This mechanism augmented in the development of new therapeutics by PTC read-through mechanism and personalized medicine. Detailed studies on NMD surveillance will possibly lead towards development of strategies for improving human health aligning with United Nations sustainable development goals (SDG 3: Good health and well-being). The potential therapeutic applications of NMD pose a challenge in terms of safe and effective modulation. Understanding the complexities of NMD regulation and its interaction with other cellular processes can lead to the development of new interventions for various diseases.

Curriculum Development for the South African Essential Steps in Managing Obstetric Emergencies (ESMOE) Anesthesiology Training Module: A Delphi Study.

The United Nations Sustainable Development Goal 3 (SDG3) for 2030 aims at <70 maternal deaths per 100,000 live births. South Africa (SA) falls short of this goal and most deaths occur in district and regional hospitals. Due to low anesthesiologist (specialist anesthetist) numbers in the public sector, the anesthetic workforce in these hospitals consists mainly of nonspecialist (general practitioner) junior doctors with limited supervision. The Essential Steps in Managing Obstetric Emergencies (ESMOE) training program for interns was introduced in 2008 to reduce maternal deaths in SA. Training is not consistently offered at all intern-training institutions and it has not been recently revised. This study sought expert suggestions to guide a revision of the regulation, pedagogy, and content of the ESMOE anesthetic module. A 3-round consensus-seeking modified Delphi technique was used whereby experts rated current and suggested learning outcomes as well as pedagogical- and regulatory aspects of the ESMOE anesthetic module in an anonymous, online, Likert-scale questionnaire. Consensus was defined as ≥70% agreement. Open-ended questions sought motivations for decisions and further suggestions for program improvement. Between March 4, 2021 and August 18, 2021, a total of 17/24 (71%), 16/17 (94%), and 15/16 (94%) consenting experts completed the first, second, and third rounds, respectively. During the 3 rounds, the panel achieved consensus on all but 2 questionnaire items. The panel agreed that ESMOE anesthesia training should be mandatory and that it should be offered through a blended learning model. They further concurred that the current learning outcomes should be retained, and suggested additional outcomes based on the most common causes of maternal mortality in SA. Attendance of the ESMOE anesthesia module should be mandatory. Its pedagogy should be updated to a blended learning style to benefit the current digital native intern generation. The content should be updated to address the main causes of maternal mortality in SA in line with SDG3. A national obstetric anesthesia guideline should be considered, and lea-dership and collaboration are required to improve the alignment of undergraduate, internship, and junior doctor anesthesia training in SA. The content, process, and stakeholder engagement suggestions in this study can assist short-course-based anesthesia workforce training in similar global contexts.

3D-printing bauxite residue/fly ash-containing geopolymers as promising metal sorbents for water treatment

Herein, we demonstrate for the first time the feasibility of employing significant amounts (up to 80 wt%) of unexplored industrial wastes (red mud and biomass fly ash) in the production of highly porous 3D-printed geopolymer lattices envisioned for wastewater treatment applications. This without compromising the mechanical performance of the geopolymers relative to those obtained using commercial precursors. The impact of the fly ash incorporation content in the fresh-state (calorimetric and reological characterization) and hardened-state (porosity and mechanical strength) properties of the produced structures was evaluated. Moreover, the influence of key printing parameters, including nozzle diameter and geometry alignment, on the resulting properties of the lattices was also evaluated. The most promising compositions were then evaluated as lead sorbents under continuous flow. The waste-based 3D-printed lattices showed remarkable adsorption ability reaching >95 % removal efficiency after 2 h. This sustainable strategy is in line with the United Nations sustainable development goals and the transition to a circular economy, reducing the consumption of natural resources and simultaneously contributing to reducing water pollution.

A Comprehensive Review of Tax Policy Changes and Tax Revenue in Kenya

Government revenue is crucial for fostering economic growth, especially in emerging economies where financial autonomy matters. Mobilising this revenue is key to achieving the United Nations Sustainable Development Goals (SDGs) focused on productivity and inclusive growth. This study analyses Kenya's tax revenue trends from 2001 to 2021, particularly the impact of tax policy reforms on the tax-to-GDP ratio. The findings reveal a significant increase in Kenya's tax revenue from 182,418 million shillings in 2001 to 1,692,662 million shillings in 2021, reflecting the effectiveness of tax reforms. It notes a shift towards direct taxes, which increased both in absolute terms and as a portion of total revenue, indicating improved compliance and administrative efficiency. Key reforms, such as the Tax Modernization Programme and the digital iTax system, enhanced revenue collection by improving compliance and tax base widening. While indirect taxes have traditionally dominated, the gap with direct taxes has narrowed, leading to a more balanced revenue structure. The study also highlights challenges, including political disruptions in 2007 and the economic effects of the COVID-19 pandemic in 2020, emphasising the need for resilient revenue strategies. Ultimately, the research concludes that continuous tax policy reform and improved administrative efficiency are essential for Kenya's fiscal sustainability and broader economic development goals.

Survival strategies of informal sector workers in Bali's tourism industry

The informal tourism sector plays a vital role in the local economies, particularly in destinations such as Bali. This aligns with the United Nations Sustainable Development Goal 8, which focuses on promoting decent work and economic growth. These workers, who are often ignored, greatly enrich the experience of tourism but encounter distinct obstacles on a daily basis. Gaining insight into their methods for staying alive is crucial. Informal workers face distinct limits compared to formal sector employees, resulting in less defined sustainability and growth strategies. A thorough investigation was carried out in nine regencies in Bali, utilizing observations and interviews, in order to examine these tactics. This method facilitated a comprehensive comprehension of their day-to-day routines and difficulties. The study identified four crucial survival strategies: implementing prudent financial management to sustain businesses and meet daily needs, utilizing communication skills to maintain income and continuity, enhancing social capital through positive relationships with fellow workers, and comprehending government policies. Out of these factors, effectively managing economic capital is crucial for maintaining livelihoods in the fiercely competitive tourism business. The results of this study provide a basis for future policies or interventions aimed at supporting and enhancing the lives of informal sector workers in tourist areas such as Bali.

The transcendentalist roots of sustainability: Ralph Waldo Emerson and the precedent for the Sustainable Development Goals education

The United Nations Sustainable Development Goals (SDGs) provide a framework for global development priorities focused on improving human well-being while protecting the environment. Though a modern initiative, the conceptual roots of sustainability and many SDG themes can be traced back over a century. This paper examines the 19th-century Transcendentalist movement and the writings of Ralph Waldo Emerson, which established a philosophical and ethical foundation closely aligned with sustainable development values. The study begins with an introduction and a brief background on the UN SDGs, followed by an overview of Transcendentalism and Emerson’s contributions. Emerson’s core principles and values—emphasis on nature, simplicity, self-reliance, and moral obligation—are explored in detail, demonstrating their parallels with the 17 SDGs. The paper then argues that Emersonian philosophy provides a foundational framework for sustainability, offering a unique perspective on integrating these transcendentalist insights into sustainability education. This study shows how American intellectual traditions anticipated today’s environmental requirements, emphasizing transcendentalism’s long-term influence and impact on achieving the Sustainable Development Goals. The study offers a unique viewpoint on sustainability by considering transcendentalism’s lasting effect and potential contribution to attaining the SDGs.

Field scale wheat yield prediction using ensemble machine learning techniques

Wheat is crucial for global food security and plays significant role in achieving United Nations Sustainable Development Goal 2 (Zero Hunger). In India, wheat accounts for 33.5 % of total cereal production. Accurate and cost effective yield predictions are essential for maintaining food security. Wheat yield forecasting is influenced by various factors, such as genotype and environmental conditions. Among these, the effect of morpho-physiological traits in the field is important for predicting yield but hasn't been studied much using ensemble machine learning methods. This study aims to bridge this gap by evaluating 29 morpho-physiological traits to predict site specific wheat yield. Big data framework was used to develop and refine several ensemble machine learning models based on field trial datasets. The developed models are optimized to reduce errors and prevent overfitting and underfitting, boosting their predictive precision. Each model's efficiency was evaluated using performance metrics such as mean absolute error, mean absolute deviation, root mean square error, R2, and overall accuracy. The ensemble model, which combines random forest (RF) and artificial neural networks (ANN), demonstrated better performance by achieving a mean absolute percentage error of 4.65 %, and R2 value of 98.48 % and 98.18 % accuracy on test data.Our results demonstrate that ensemble models combining RF with support vector regression (SVR) outperformed individual models such as RF, SVR, ANN, multivariate adaptive regression splines (MARS). These findings are a promising step towards future research focused on creating more advanced ensemble methods with finely tuned hyperparameters for improving the accuracy of large-scale wheat yield prediction.

ADVANCED SMART PARKING BARRIERS: DESIGN, IMPLEMENTATION, AND IMPACT ON URBAN INFRASTRUCTURE

This study investigates the creation and execution of an intelligent individual parking barrier system aimed at resolving urban parking difficulties in Malaysia. The project adheres to a systematic methodology that includes conceptual design, detailed design, prototype creation, testing and validation, data analysis and refinement, and implementation and deployment. The system's lifetime and efficacy are guaranteed by the inventive use of durable materials and advanced sensor technologies. Additionally, convenience and security are enhanced by a user-friendly wireless control system. The data analysis confirms that the system is highly reliable, with sensors that have a 95% success rate in detecting events and low delay in wireless control operation. The concept is in line with larger smart city projects, such as the Kuala Lumpur Smart City Blueprint, which aims to promote parking options that are both efficient and secure. In addition, the system promotes environmental sustainability by minimizing emissions related to the search for parking. It also aligns with the United Nations Sustainable Development Goals, namely SDG 11 (Sustainable Cities and Communities) and SDG 13 (Climate Action). This study showcases the possibility of implementing the proposed system on a large scale in different urban areas in Malaysia, therefore leading to the development of more intelligent and effective urban infrastructure.

Assessing the Impact of a College Service-Learning Course Focused on the United Nations Sustainable Development Goals

Liberal arts education in the United States seeks not only to prepare students for a specific career but also to develop well-rounded citizens. As part of the core liberal arts curriculum at Lynn University, all students must complete a service-learning course known as the Citizenship Project. A core component of the course is a student-designed service-learning project executed each year. In 2019, the Citizenship Project was revised to align with the United Nations Sustainable Development Goals (SDGs), ensuring that each project would seek to contribute to one of these goals. The purpose of this study is to examine which SDGs the service-learning projects addressed and summarize how the projects contributed to the SDGs.

Prevalence of child maltreatment in a nationwide sample of 18 to 31 year-olds in Germany

BackgroundChild maltreatment (CM) can have devastating and potentially lifelong effects for those affected and is a major contributor to mental health problems. To tackle public health problems it is crucial to have reliable data on CM. The aim of this study is to assess the prevalence and predictors of CM in a nationwide sample of the German population of young adults.MethodsThe study population (young adults aged 18 to 31 years) stems from the KiGGS Cohort study, the longitudinal branch of the German Health Interview and Examination Survey for children and adolescents. This sample meets the criteria of the United Nations Sustainable Development Goals (SDG) indicator 16.2.3. The data was collected between 2014 and 2017. CM were assessed with the Childhood Trauma Questionnaire (CTQ) in. In addition, socio-demographic variables and other known risk factors for CM were assessed. A total of 6433 (47.8% female) participants were included in the analyses. Binary logistic regression analyses were used to investigate predictors of maltreatment subtypes. Ordinal regression was used to examine their association with experience of multiple forms of CM.ResultsOverall, 18.4% (f: 20.9%, m: 16.1%) of the participants reported having experienced at least one type of CM; 6.7% (f: 8.8%, m: 4.8%) reported experiences of emotional abuse, 3.7% (f: 3.9%, m: 3.5%) physical abuse, 3.5% (f: 5.3%, m: 1.7%) sexual abuse, 9.0% (f: 9.9%, m: 8.2%) emotional neglect and 8.6% (f: 8.5%, m: 8.7%) physical neglect. Gender, subjective social status, education and household dysfunction (e.g. living with an individual who is using substances) emerged as significant predictors for different types of CM. Additionally, all these factors were significant risk factors for experiencing cumulative CM.ConclusionsCM is common in the German population, with almost one in five people experiencing at least one type of CM. The results reveal important risk factors for the occurrence of CM. In particular, people with lower social status and those who grew up in dysfunctional households are at higher risk of CM. Greater support for this vulnerable population may reduce the prevalence of CM.

Perspectives of the role of ICRP and the system of protection in meeting the United Nations sustainable development goals

Established in 2015 the United Nations (UN) sustainable development goals (SDGs) were agreed with the aim to balance the need to address social and ethical obligations such as ending poverty and other deprivations, while tackling climate change and the other planetary boundaries. In 2018 the International Commission on Radiological Protection (ICRP) initiated a review and revision of the System of Radiological Protection which will lay the foundation for Radiation Protection standards, regulations, guidance and practice worldwide for the next 40 years. Recognising the importance of the UN SDG's the ICRP has started to consider what the role of the revised system of protection should be in enabling delivery. On the 15 May 2024 the Society for Radiological Protection and World Nuclear Association ran a workshop exploring the intersection of the System of Radiological Protection and the SDG's. The outputs of the workshop are summarised in this paper showing the views from a variety of practitioners working across the radiation protection sectors on the key factors to be considered in the revision of the system of radiological protection to enable delivery of the UN SDG's.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s “Dual Carbon” Goals (DCGs means the goals of “Carbon Peak and carbon neutrality”), this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda (IDZSDAs), combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity (USDC). After obtaining USDC assessment results through the assessment system, an approach combining Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) based on machine learning is proposed for identifying influencing factors and characterizing key issues. Combining Coupling Coordination Degree (CCD) analysis, the study further summarizes the systemic patterns and future directions of urban sustainable development. A case study on the IDZSDAs from 2015 to 2022 reveals that: (1) the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process; (2) the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities; and (3) the machine learning-based combined recognition method is scalable and dynamic. It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

Leveraging ChatGPT for Sustainability: A Framework for SMEs to Align with UN Sustainable Development Goals and tackle sustainable development challenges

Abstract The United Nations Sustainable Development Goals (SDGs) outline a global agenda for sustainable development, but need more detailed implementation guidelines for businesses, particularly Small and Medium Enterprises (SMEs). Given their limited resources, SMEs face significant challenges in adopting sustainability practices aligned with the SDGs. This study explores the potential of ChatGPT, a large language model, to assist SMEs in overcoming these challenges. The research introduces a ChatGPT-aided framework through a novel methodological approach to help SMEs develop sustainability roadmaps, engage stakeholders, and identify key sustainability goals, risks, opportunities, and Key Process Indicators (KPIs). The case study of an SME in the electronic measurement equipment industry is used to validate the framework. The findings, corroborated by a Focus Group with the participation of academics and SME top managers, demonstrate the framework’s potential to enhance SME sustainability practices, contributing to academic discourse and offering practical insights that will inform and empower industry stakeholders. Furthermore, several actions are presented to respond to concerns about the accuracy and reliability of AI-generated recommendations. Finally, future research should seek to validate the proposed framework across a broader range of industries and SME contexts and assess this methodology’s application with organisations other than SMEs.

The Relationships between Job Performance, Job Burnout, and Psychological Counselling: A Perspective on Sustainable Development Goals (SDGs)

Job burnout (as an independent variable) and job performance (as a dependent variable) are often correlated, and one of the main arguments is that psychological counseling can mitigate job burnout, which in turn influences job performance. This article aims to introduce a new perspective on the subject by establishing a new paradigm in the field. It also explores the role of psychological counseling as a precautionary measure. Furthermore, it aligns with the United Nations Sustainable Development Goals (SDGs) 3 and 8, which seek to enhance public health, collective well-being, and decent work or at least equitable workplace conditions. Adopting a quantitative analysis method, this study used panel data from 2020 to 2023 and group regression to analyze these data. The instruments comprise job performance (KPI) and mental health records. This study revealed that job burnout as experienced by academics is regulated by their job performance (β = −0.013, p &lt; 0.001. This study revealed that academics’ job performance remained unchanged despite the number of psychological counseling and interventions conducted, so consequently, psychological counseling and interventions do not affect performance directly. This research contributes to the literature by utilizing a novel measurement approach. It is concluded that competency is the key to having a decent work environment and staff experiencing collective well-being.

Eco-friendly chemometric analysis: Sustainable quantification of five pharmaceutical compounds in bulk, tablets, and spiked human plasma

The development of sustainable chemometric methodologies is crucial in pharmaceutical analysis to ensure accurate chemical quantification with little environmental effect. This study introduces novel chemometric techniques for the simultaneous measurement of Rabeprazole (RAB), Lansoprazole (LAN), Levofloxacin (LEV), Amoxicillin (AMO), and Paracetamol (PAR) in various samples, including lab-prepared mixtures, tablets, and spiked human plasma. Taguchi L25 (5^5) orthogonal array design to construct the calibration and validation sets was employed, the techniques used named Principal Component Regression (PCR), Partial Least Squares (PLS-2), Artificial Neural Networks (ANNs), and Multivariate Curve Resolution-Alternating Least Squares (MCR-ALS). The models were validated for their effectiveness in resolving complex spectra from overlapping components, the models achieving high correlation coefficients (R≥0.9997) and the relative error of prediction (REP) and root mean square error of prediction (RMSEP), were (0.2221 to 0.8022) and (0.0352 to 0.1767), respectively. The bias-corrected mean square error of prediction (BCMSEP) was (−0.00065 to 0.00166). The application of various assessment tools for evaluating greenness, blueness, and whiteness offered valuable insights into the environmental impact and sustainability of the developed methods compared to the reported ones. According to the Need Quality Sustainability (NQS) index, the developed methods align with sustainable analytical practices and support the United Nations Sustainable Development Goals (UN-SDGs).

A tale of two generations: Justice in cities for a low-carbon world

According to the greenhouse gas (GHG) protocol for cities, GHGs are responsible for an estimated 75 per cent of global energy-related carbon dioxide emissions. This represents a key opportunity to tackle climate change. With the year 2023 and, in particular, the month of July delivering record-breaking temperatures, this demonstrates a clear need to accelerate decarbonisation and, in this context, to reduce the carbon footprint of cities. Further, acting on urban development addresses a number of key United Nations Sustainable Development Goals (UNSDGs). Many cities, however, are struggling to break away from a reliance on carbon and some are economically dependent on it. This paper seeks to investigate, through an energy justice lens, some first considerations of how to secure just and sustainable urban regeneration. It posits that energy justice, and its five core principles, is a useful analytical tool for considering the justice and development concerns related to the transition to a low-carbon world. The cities examined in brief in this comparative study are Bordeaux in France, Venice in Italy, Ho Chi Minh City in Vietnam and Bangkok in Thailand. The research posits that despite different trajectories, cities can play a leading role in ensuring justice and sustainability in our low-carbon world.