Abstract
All international agreements recognise that sustainable development, equity and poverty alleviation are preconditions for the substantial societal and technological transformations required to limit global warming to 1.5°C. A growing body of literature indicates that while climate change undermines the progress of Sustainable Development Goals (SDGs), climate actions also pose several trade-offs with them. Climate adaptation has a largely synergistic relationship with SDGs across various socio-economic contexts. However, climate mitigation’s relationship with SDGs is far more complex. While the need to decarbonise is universal, the pathways to deliver deep decarbonisation vary across contexts and scales and are located within the local socio-economic realities besides local environmental factors. This paper argues that (1) climate mitigation measures in countries like India – with rising income inequality and high social diversity in caste, religion and region – need a tailored assessment approach, (2) carefully mediating climate mitigation measures – like deep decarbonisation – at the local level is crucial to enable transformative change required to meet the Paris Agreement and the UN Agenda 2030, (3) enabling ‘just’ deep decarbonisation or SDG-enabled decarbonisation at the local level requires addressing unmet needs of the vulnerable population even at the cost of increased emissions, and (4) sector-specific decarbonisation strategies at the national level must be translated into the local area’s social, economic, environmental and institutional realities. This paper grounds this approach using the example of the transport sector and applies it in a mid-sized city of India, Udaipur, to illustrate the argument.
Key messages
Climate mitigation efforts to limit global warming to 1.5°C must be assessed against the SDGs.
Climate mitigation pathways may have a synergistic and a contradictory relationship with the SDGs.
Any city’s transport choices possess strong relationship with the SDGs, and urban equity.
SDG-enabled decarbonisation pathway prioritises mobility demand of vulnerable groups.
Context setting
Equity is a central theme in international and climate change law in the context of sustainable development and poverty alleviation (Bodansky et al, 2017). The Paris Agreement recognises the principle of equity’s significance in enabling an effective response to climate change. The Intergovernmental Panel on Climate Change’s (IPCC’s) Assessment Report (AR) 5 highlights that sustainable development, equity and poverty alleviation are preconditions for the substantial societal and technological transformations required to limit global warming to 1.5°C. The IPCC’s AR5 (IPCC, 2014) and AR6 (IPCC, 2023) mainstream the idea of syncing climate action with development goals, represented through the United Nations Sustainable Development Goals (SDGs). A growing body of literature indicates that while climate change undermines the progress of SDGs, climate actions also pose several trade-offs with SDGs (Denton et al, 2014). Climate adaptation has a largely synergistic relationship with SDGs across various socio-economic contexts (Roy et al, 2022); adaptation measures to extreme weather events, climate-proofing houses and addressing urban heat island effects are a few examples of how climate adaptation generates co-benefits for human health and positively interacts with several SDGs. However, climate mitigation’s relationship with SDGs is far more complex (IPCC, 2022); the IPCC’s 1.5°C Report and AR6’s Working Group III (WGIII) Report (IPCC, 2022) highlight how mitigation measures consistent with the 1.5°C pathway risk the achievement of sustainable development in regions with high dependence on fossil fuels. Furthermore, effective deployment of climate mitigation measures depends on a region’s social, cultural, economic and institutional contexts, requiring a thorough assessment of how it unfolds across different scales (from national to local). A growing body of research indicates climate mitigation is governed by a myriad of local socio-economic and environmental factors like local weather, ecology and topography; climate vulnerabilities and susceptibilities; socio-economic realities and level of inequalities; pro-activeness of the state in addressing development challenges; and community organisation.
The United Nations Environment Programme’s (UNEP’s) Emissions Gap Report 2022 highlights the lack of context-sensitive and credible pathways to meet the Paris Agreement, despite the inclusion of alternative development pathways like IPCC’s Climate-Resilient Development Pathways (UNEP, 2022).1 The report calls for ‘urgent system-wide transformations’ by 2030 to limit the warming by 1.5°C; and states that compared to current climate policies, an additional 45 per cent emission reduction is crucial. Amid this, deep decarbonisation – a climate mitigation measure – has emerged as vital elements of national climate goals globally. While the need to decarbonise is universal, the pathways to deliver deep decarbonisation vary across contexts and scales. For example, India’s Deep Decarbonisation Pathways Report (Shukla et al, 2015) illustrates ensuring social equity (of those deprived of energy, resources and opportunities) while decarbonising increases emission. It shows that although the GDP efficiency of energy increases, gross emissions also increase in the sustainable scenario (includes social equity) compared to the conventional scenario.
Given this backdrop, we argue: (1) climate mitigation measures in countries like India – with rising income inequality and high social diversity in caste, religion and region – need a tailored assessment approach; (2) carefully mediating climate mitigation measures – like deep decarbonisation – at the local level is crucial to enable the transformative change required to meet the Paris Agreement, SDGs and other international agendas; (3) enabling ‘just’ deep decarbonisation or SDG-enabled decarbonisation at the local level requires addressing the unmet needs of the vulnerable population even at the cost of increased emissions; and (4) sector-specific decarbonisation strategies at the national level must be translated into the local area’s social, economic, environmental and institutional realities. This paper aims to frame more effective and holistic climate mitigation strategies at the local level by illustrating the these ideas via Udaipur City’s Transport Decarbonisation Pathways. We chose the transportation sector to ground this theory as it the fastest-growing sector in terms of consumption demand and emissions, and currently contributes to about a quarter of global emissions. India is the third-largest contributor of global transport sector emissions, making low-carbon transport a priority for India’s nationally determined contributions and other national climate mitigation strategies. Furthermore, transport demand in countries like India is often representative of its social fabric. We chose Udaipur as its unique urbanisation characteristics speak to cities of the Global South as well as the North; like most cities in the Global South, Udaipur is on the brink of rapid urbanisation and increased motorisation, offering ample opportunity to reshape its urban transport landscape; and like numerous cities in the Global North (especially Europe), Udaipur possess characteristics of smaller, compact cities with historic urban cores and a tourism-centric economic base. Moreover, Udaipur’s population composition is representative of about 70 per cent of Indian cities.
Contextualising transport decarbonisation and SDGs interactions in Indian cities
As discussed, deep decarbonisation transport pathways for cities must attempt to achieve the SDGs simultaneously. The need for this is much higher in the Global South than in the Global North. The defining difference between the two is that ‘access for all’ is a lived experience, accompanied by high transport demand in the Global North. In the Global South, high levels of inequities (of income, caste, ethnicity and religion) and exclusions limit transport availability to the vulnerable (urban poor, women, socially disadvantaged, differently abled), reducing the possibility of meeting the SDGs. This section conceptually presents the synergies and conflicts between decarbonising transport and the SDGs rooted in our understanding of Indian cities.
Transport increases access to basic services, and economic and civic opportunities, enhancing quality of life; if rapidly expanding urban areas are not served by affordable, reliable and safe public transport, they deepen the effects of peripheralisation of low-income households by increasing instances of poverty (Coelho et al, 2022) (SDG 1), women’s harassment (SDG 5) and road accidents (SDG 3). Transport connectivity increases access to healthcare facilities, improving general health outcomes (SDG targets 3.3 and 3.8) and reducing neonatal and maternal mortality (SDG targets 3.1 and 3.2). Rapid motorisation leads to numerous negative externalities like air pollution and decreased physical activity, increasing the disease burden. Motorisation increases its conflicts with SDG 3: India only has a 1 per cent share of the world’s vehicles yet hosts 11 per cent of the global road fatalities – the highest in the world – with non-motorised transport (NMT) users being the worst hit user group. More than half the population in urban India depends on walking or cycling (Soman et al, 2019), yet Indian cities fare poorly in NMT infrastructure, requiring large-scale improvements. Hence, redesigning and re-engineering streets to ensure equitable distribution of road space is vital for adopting low-carbon modes like active and public transport. Affordable and clean public transport accompanied by last-mile connectivity through active or intermediate public transport (for example cycle-rickshaws and e-rickshaws) would enhance the mobility of all (SDG 11), reduce air pollution (from vehicular trips and traffic congestion) and improve cardiovascular health (SDG 3).
Pedestrians are often low-income women whose access to opportunities is curtailed by unresponsive transport systems (unsafe, unreliable and unaffordable), causing implications for SDG 5 and SDG 1, as women’s workforce participation is linked to improved economic outcomes for their households (Mahadevia, 2015). However, urban India’s unaffordable, unsafe and unreliable transport systems contribute to capping the urban female workforce participation at only 7 per cent (Sharma, 2020). Increasing women’s access to economic and civic opportunities requires regular safety audits of all transport system components.
Besides access to employment (SDG target 8.1), efficient transport systems are fundamental for economic productivity (SDG targets 8.2 and 8.5). Transport is also a significant employment sector (like India’s autorickshaw industry), creating ample ‘decent-work’ opportunities for low-skilled labourers (SDG target 8.10). In contrast, traffic congestion burdens the economy heavily; it affects workers’ productivity by forcing them to lose more time and fuel in commuting, resulting in increased household transport expenditure, emissions and stress (UNHSP, 2020). Thus, provisioning affordable low-carbon public transport has multiple synergies with the SDGs.
At the same time, while enhancing public transport provisioning, we need to be aware of its negative implications on the SDGs. For example, mass-transit projects often generate trade-offs with cultural and natural heritage in densely built Asian cities (SDG targets 11.3 and 11.4) (Bennett, 2017). In cities across the Global South, transport systems are susceptible to extreme weather events, curbing mobility for vulnerable groups, often dependent on public transport systems (SDG targets 11.5 and 11.b; UN-Habitat et al, 2015). Mega transport projects, that is, the mass-transit projects or transit-oriented development proposed to increase multifold transit ridership when implemented in the dense and informal cities of the Global South, create large-scale displacements, leading to livelihood loss; rupture of the social fabric (networks) and hence increase in delinquency; and increased transportation expenditure, which in turn curbs physical, social and economic mobility. Thus, mega transport projects may marginalise vulnerable populations while mitigating climate change. Hence, such projects must actively mitigate the adverse socio-economic impacts of mega transport projects.
The three transport scenarios – Udaipur
Udaipur, located in the western state of Rajasthan, is known for its history, culture and institutions. Along with a population of 1.5 million, Udaipur hosts a vast floating population of students and tourists. Traditionally a compact city (a 12-minute city) with a ring-radial road network, Udaipur is rapidly expanding along two highways, creating high travel demand. The walled city and its immediate surroundings are predominantly mixed use; land use becomes largely residential moving towards the peripheral areas except the eastern ones, which are industrial. Roads are highly congested and contested with heterogeneous road users: motorised vehicle users, pedestrians, cyclists, street vendors and their clientele, private bus operators and their clientele, pavement dwellers, street parking and so on. Over time the city has experienced a steep increase in motorisation; the number of registered vehicles increased by 52 per cent in a span of six to seven years. About half of the total trips in Udaipur are on foot; yet NMT infrastructure including footpaths, cycle lanes/tracks, pedestrian/cycle crossings, and street lighting are poorly designed and inadequate. Less than 1 per cent of the roads have cycling and footpath infrastructure. Public transport in Udaipur mainly comprises shared and personal autorickshaws, along with a few city buses. The city also has autorickshaws (three-wheelers): these are used as intermediate public transport (IPT) and mostly operate under fixed routes and rates mechanisms, essentially serving as public transport (PT). There are 27 designated IPT routes and 87 IPT stands across the city. Some 11 per cent of the total trips are made by IPT. City buses operate on five routes with 89 kilometres of total route length and 2 per cent mode share. Some 60 per cent trips of low-income groups are on foot, 66 per cent of all female trips are on foot – indicating captive users. Low-income women in Udaipur have the lowest trip rate (proxy for mobility).
We argue our case by presenting three decarbonisation scenarios for the city’s transport: (1) business as usual (BAU), (2) a decarbonised option, which is a technology-enabled scenario, and (3) an SDG-enabled decarbonised transport option that proposes an improvement in the mobility of a segment of the population – particularly women – experiencing low levels of mobility. The first two, the BAU scenario and the Technology (decarbonised) scenario, are adopted from Udaipur’s Low-Carbon Mobility Plan (LCMP) (Urban Mass Transit Company Ltd, 2013). The BAU scenario in Udaipur is rendered by rapid motorisation and road-based solutions due to the absence of transport demand management or NMT and PT system improvements. The Technology scenario considers a traditional decarbonisation pathway that minimises travel demand via land-use changes and promotes cleaner technologies. The SDG-enabled scenario proposes improving the mobility of the ‘mobility-deprived’ segments of the population. This translates into improved women’s work participation rates, enhanced travel demand, improved education levels, enhanced education trips to schools and tertiary educational institutions, and improved access to healthcare facilities for all, especially older adults. Building on the Technology scenario’s interventions, the SDG-enabled scenario includes large-scale NMT and PT infrastructure improvement, street redesign based on Universal Design and Complete Streets guidelines, improved household access to PT/IPT, and improved mobility of vulnerable groups (Ministry of Housing and Urban Affairs and ITDP, 2019). Travel demand projections were calculated using assumptions about population growth rate, trip rate, trip length and mode share for each trip purpose applied to Udaipur’s LCMP for the target year 2041. Choosing 2041 as the target year allows consistency with the LCMP and sufficient time to enable the long-term intervention mentioned earlier. A detailed scenario break-down is presented in Table 1.
Transport system parameters across scenarios
Transport systems parameters | BAU scenario | Technology scenario | SDG-enabled scenario | |
---|---|---|---|---|
Trip rate | Interzonal trip rate | 1.12 | 1.12 | 1.17* |
Mode share (%) | Walk | 20.00 | 28.00 | 38.00 |
Cycle | 2.00 | 9.00 | 9.00 | |
2W | 51.00 | 20.00 | 15.00 | |
3W | 21.00 | 10.00 | 8.00 | |
4W | 4.00 | 1.00 | 1.00 | |
Buses | 2.00 | 32.00 | 30.00 | |
Accessibility | % HH within walking distance of PT/IPT stops | 60 | 83 | 100 |
Level of service (scale 4–1; 4 = lowest, 1= highest) | 4 | 2 | 1 | |
Perception of safety while using NMT | Walking | 8% | 83% | 100% |
Cycling | 7% | 83% | 100% | |
Level of service (scale 4–1; 4 = lowest, 1= highest) | 4 | 2 | 1 | |
Vehicle kilometres travelled | Annual motorised VKT | 2,559,907 | 1,335,210 | 2,003,526 |
Emissions levels (tons annually) | NO2 | 61,261 | 36,066 | 20,298 |
CO₂ (million) | 33 | 24 | 32 | |
PM10 | 18,000 | 10,484 | 5,721 | |
SO2 | 803 | 591 | 235 | |
CO | 695,935 | 454,591 | 211,599 | |
Mitigation results | % VKT reduction | n/a | 48% | 22% |
% emission reduction (CO2) | – | 27% | 3% | |
% emission reduction (other GHGs) | – | 35% | 64% | |
SDG interactions | SDG 1 (No Poverty) | |||
SDG 3 (Health and Well-being) | ||||
SDG 5 (Gender Equality) | ||||
SDG 8 (Economic Development) | ||||
SDG 11 (Sustainable Cities) | ||||
SDG 13 (Climate Action) |
Notes:
* The SDG scenario accounts for a total trip rate of 1.78; due to high intrazonal trips for user groups like elderly, women, children and youth, the interzonal trip rate is 1.17.
2W = two-wheeler vehicles or two-wheelers, 3W = three-wheelers, 4W = four-wheelers; BAU = business as usual; GHG = greenhouse gases; HH = household; IPT = intermediate public transport; NMT = non-motorised transport; PT = public transport; SDG = Sustainable Development Goals; VKT = vehicle kilometres travelled.
Future scenarios’ discussion
The BAU scenario projects high emissions (33 million tonnes annually), unequal distribution of road space, and a chaotic transport landscape, generating a negative relationship with four SDGs: SDG 1 (No Poverty), SDG 3 (Health and Well-being), SDG 5 (Gender Equality) and SDG 11 (Sustainable Cities). About 40 per cent of households, largely low-income households, lack access to public transit, affecting their access to economic opportunities. Perception of safety while using NMT is very low (only about 8 per cent of users feel safe), affecting mobility of the NMT-dependent population (largely comprising women, urban poor people and elderly people).
The Technology scenario consisting of interventions currently promoted by the national government (electric vehicles (EVs), efficient fuel and engine standards) results in massive emission reduction (27 per cent more reduction than BAU; a total of 24 million tonnes annually). However, it does not focus on improving mobility of the vulnerable population groups (that is, poor people, women, specially-abled people, elderly people and children), causing trade-offs with SDG 1, SDG 5 and SDG 11. This scenario positively correlates with SDG 3 (Health) and SDG 13 (Climate Action) owing to emission reduction. Increased EV adoption is likely to generate employment in EV manufacturing, generating a synergy with SDG 8 (Economic Development).
The SDG-enabled decarbonised scenario is socially sensitive, which prioritises the mobility demand of those with low or no mobility, enhances mobility by retaining the share of NMT and PT, and regulates excessive use of private motorised vehicles. Compared to the BAU scenario, the SDG-enabled scenario offers a 22 per cent reduction in vehicle kilometres travelled (VKT), a 3 per cent reduction in CO₂ emissions, and a 64 per cent reduction in other greenhouse gases (GHGs). But compared to the Technology scenario, it results in a 26 per cent increase in the VKT, a 24 per cent increase in CO₂ emissions and a 29 per cent decrease in other GHG emissions. However, the SDG-enabled scenario generates synergies with all the six selected SDGs. Compared to the BAU and Technology scenarios, SDG-enabled scenarios offer:
Improved women’s workforce participation rate to 33.7 per cent from 19.6 per cent in the BAU scenario, increasing their mobility for work and other purposes (SDGs 5 and 8) and increasing their work trips to two per day.
100 per cent enrolment of all school-going children (ages 3 to 17) (SDGs 1 and 8).
Improved NMT infrastructure and safety (against violence and accident) by 100 per cent compared to 8 per cent in BAU, and increase in trips for ‘recreational’, ‘healthcare’ and ‘other’ purpose trips among women.
Increased integration of all PT and IPT modes (city buses, minibuses, shared cars, autorickshaws) encouraging a shift to PT and IPT from personal vehicles; 2Ws (two-wheelers) and 4Ws (four-wheelers) mode shares decrease by 37 per cent and 3 per cent, respectively.
Enhanced last-mile access to PT, resulting in 100 per cent household access to PT (compared to 60 per cent in BAU), and increased PT mode share by 28 per cent.
Increased government subsidies and aggressive promotion of EVs resulting in 100 per cent PT and IPT fleet transition to EVs.
Intrazonal trips (short distance trips), increased by 49 per cent; all intrazonal trips transitioned to NMT – truly enabling a 12-minute walkable city.
Average trip rate improved from 1.12 to 1.78, indicating massive improvement in mobility, especially for vulnerable groups like women, urban poor people, elderly people and children.
Conclusion
If equity is considered a central theme in sustainable development and poverty alleviation – as highlighted by the Paris Agreement and the IPCC’s AR5 and AR6 – while making efforts to limit global warming to 1.5°C, mitigation efforts must be assessed against the SDGs. While climate adaptation’s synergistic relationship with the SDGs is well understood, climate mitigation pathways can cut both ways; these could negatively impact progress on the SDGs, which then require policy interventions or could have synergies with the SDGs. This article, taking the example of decarbonised transport for a mid-sized city in India, shows that the transport choices taken by a city have a strong relationship with the SDGs. Besides, it also shows that transport can offer possibilities of achieving and improving the SDG targets. Broadly, this article shows that the SDG-enabled decarbonisation pathway is socially sensitive, prioritises the mobility demand of vulnerable groups, enhances mobility for all but by retaining the share of NMT and PT, and regulates excessive use of private motorised vehicles.
Climate mitigation at the cost of SDG improvement cannot be a path the Global South should take, particularly in the cities of South Asia and Africa, where much of the future urbanisation will occur. Enhancing the mobility of the mobility-deprived population means developing housing solutions along with the transport solutions. Housing solutions for the low-income populations cannot happen if the provisioning is through speculative land and housing markets. It requires that housing is considered a social good, which takes us to looking at urban land as one with social value and not just market value. Enhancing the mobility of women, in particular those of low-income households, also means an increase in safety in the city. The solutions to SDG-enabled decarbonised transport in cities of the Global South have to seek solutions outside the transport sector into ideas of ‘Right to the City’.
Note
IPCC’s Climate-Resilient Development Pathways (CRDP) provide a roadmap for integrated socio-economic and climate mitigation goals across different scales.
Funding
This work was supported by Department of Biotechnology, Ministry of Science and Technology, India (BT/IN/TaSE/74/MP/2018-19).
Acknowledgement
As this publication is a part of the Opportunities of Climate Mitigation and Sustainable Development (OPTIMISM) project, we extend our gratitude the remaining members of the OPTIMISM India team: Minal Pathak for guidance on project methods, Kanika Gounder for assisting in city-level assessments, and Bandish Patel for administrating fieldwork and stakeholder engagement. This paper was written when Chandrima Mukhopadhyay (co-author) was associated with the OPTIMISM Project as a Senior Research Associate.
Data availability statement
The authors takes responsibility for the integrity of the data and the accuracy of the analysis. Data provided in the supplemental material can be used by other researchers for further work.
Conflict of interest
The authors declare that there is no conflict of interest.
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