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Copyright © 2023 United Nations Environment Programme
ISBN No: 978-92-807-4015-8
Job No: DEW/2513/NA
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Recommended citation: “United Nations Environment Programme (2023). Measuring Progress: Water-related ecosystems and the SDGs. Nairobi.”
Layout: UNON Publishing Services Section
Project Manager: Therese El Gemayel, Early Warning and Assessment Division, UNEP
The drafting was guided by an Expert Group chaired by Dr. Erica Gaddis, University of Utah.
UNEP overall coordination: Therese El Gemayel, Ludgarde Coppens and Brennan Van Dyke from the UNEP Early Warning and Assessment Division under the leadership of Jian Liu, Director of the Early Warning and Assessment Division.
UNEP publication support team: Angeline Djampou (library); Dany Ghafari (data processing); Jinita Dodhia (UNON, design and layout); Karl Scheifinger (drafting, data visualization, peer-review); Moses Kiget (maps development); Ralf Heidrich, (drafting, peer review coordination).
Scientific editing: Strategic Agenda
Financial support from the European Union to produce this report is gratefully acknowledged.
The 17 Sustainable Development Goals (SDGs) and 169 targets of the universal 2030 Agenda offer a blueprint for a sustainable and resilient future, where responsible management of our planet’s finite resources can create a brighter tomorrow for future generations. Global crises and conflicts, including the COVID-19 pandemic, the war in Ukraine, exacerbating food, energy, humanitarian and refugee crises, and a full-fledged climate emergency make it ever more important to redouble our efforts to implement this blueprint for building back a better world. Today at the half-way point, the latest available data and estimates for 92 environment relevant SDG indicators tell us that the world is not on track to achieve the environmental dimension of the SDGs by 2030. However, there is some positive news. Global data availability increased to 59 per cent in 2022, from 34 per cent in 2018 and 42 per cent in 2020. And although only 38 per cent of the environment-related indicators indicate environmental improvement, this is a solid improvement compared to only 28 per cent in 2020. Moreover, indicators of some Goals showed strong positive trends, including SDG 9 on infrastructure, SDG 7 on energy, and SDG 6 on freshwater. The integrated and indivisible nature of the SDGs reflect the interlinked nature of land- and water-based ecosystems, and the rich biodiversity they support, which provides food, clean water and air, and raw materials that fuel economic growth, leading to prosperity and human well-being. Achieving the SDGs, therefore, requires an integrated approach that recognizes how these challenges—and their solutions—are interrelated. The third edition of the Measuring Progress report focuses on statistical methods to understand these interlinkages through the prism of water ecosystems and the SDGs. In so doing, it provides insight into how freshwater- and marine-related ecosystems are impacted by various drivers, pressures, and actions, through use of statistical analysis. We hope that this report will encourage governments to strengthen further their statistical capacity in relation to the environment, incite further work on developing disaggregated and ecosystem-relevant data and on promoting use of non-traditional data sources like big data and citizen science. The all-sectoral approach encouraged by the SDGs, which Measuring Progress systematically explores would reinforce synergies as much as minimize trade-offs for a more holistic realization of the 2030 Agenda. With every action we take or investment we make in a sustainable future, we inch closer to the world we all want to see. With unwavering hope and determination, let us continue to strive towards realizing the 2030 Agenda.
Jian Liu Director, Early Warning and Assessment Division
The United Nations Environment Programme’s (UNEP) Measuring Progress series of reports provides an overview of the progress made in data availability for the 92 environment-related Sustainable Development Goal (SDG) indicators, coupled with improvement or degradation in the trend of each indicator. It also explores the potential and limitations of using statistical analysis to demonstrate interlinkages between indicator pairs to better inform policymakers of the synergies and trade-offs between SDGs. The indicators are divided into four categories:
This report explores the use of multivariate statistical analysis using waterrelated ecosystems (freshwater and marine) as an example of the utility of this approach to explore how ecosystems are impacted by drivers, pressures and actions at multiple scales.
Global analysis of the progress of the 92 environment-related SDG indicators demonstrates an improvement in data availability, resulting from additional data being reported by countries leading to the availability of sufficient data to aggregate at regional and global levels. In 2022, the environment-related SDG indicators with sufficient data to analyse were estimated at 59 per cent, up from 42 per cent in 2020 and 34 per cent in 2018. Indicators with more data available are mostly found in SDG 6 on freshwater, SDG 7 on energy, SDG 12 on sustainable consumption and production, SDG 13 on climate change, SDG 14 on life below water and SDG 15 on life on land, with the most improvement in data availability reported in the Latin America and Caribbean, Northern Africa, and Europe regions.
This major improvement in data availability results from a sustained investment by countries in their national statistical systems to collect and report data for SDG indicators as part of their sustainable development programmes, supported by capacity development efforts by custodian agencies.
The further development of methodologies that use new data sources also contributes to improved data availability. Many national statistical offices (NSOs) are already experimenting with using big data in the production of official statistics. Currently, the dominant big data types include Earth Observation (EO) data, citizen science data and other sensor network data, combined with advanced analytical techniques (e.g. machine learning, geospatial modelling and geostatistical modelling).
In 2022, at the global level 38 per cent of the 92 environmentrelated indicators showed positive change, indicating environmental improvement, and 21 per cent showed little or negative change. The most indicators showing positive trends were those related to SDG 9 on infrastructure, SDG 7 on energy and SDG 6 on freshwater.
The regions with the highest proportion of SDG environmentrelated indicators showing environmental improvement are the Latin America and the Caribbean region (39 per cent) and the Central and Southern Asia subregion (38 per cent). The regions with the lowest proportion of indicators showing environmental degradation are Central and Southern Asia (12 per cent), Western Asia (13 per cent) and Northern Africa (14 per cent).
While measuring the progress of the 92 environment-related SDG indicators focuses on evaluating trends, it does not assess the magnitude of the trends or progress towards meeting targets associated with specific indicators.
This report advances the statistical methods to better assess and understand the interlinkages between pairs of indicators through the use of multivariate statistical analysis. This builds on the methods used in the previous report, Measuring Progress: Environment and the SDGs, which explored the use of correlation analysis to identify the interlinkages between pairs of indicators. Based on the driver-pressure-state-impact-response (DPSIR) framework, the analysis identifies how one state of the environment indicator is relates to indicators of a multitude of drivers of change as well as socioeconomic and environmental factors. The statistical analysis focuses on freshwater- and marine-related ecosystems and is conducted at the global, national (Colombia and Mongolia) and basin (Poyang basin, China) levels.
Global policy discussions benefit from new analytical approaches to understanding the underlying interlinkages and drivers of indicator trends. The analytical approach used has the potential to contribute to a more policy-relevant integrated analysis. The analysis confirmed many known interlinkages between freshwater- and marine-related ecosystems and variable drivers. It also identified several new interlinkages that cannot be easily explained with the existing literature, requiring further investigation to identify whether these are covariates or newly identified drivers. Consideration of these new drivers may be highly relevant to the development of new innovative policies to protect these ecosystems.
Evaluating indicators at the national level provides a more comprehensive and actionable interpretation of key interlinkages than at the global level, but global-level trends remain critical to assessing overall progress in achieving the SDGs. A unique aspect of the analysis is the inclusion of both global-level and nationallevel interlinkages. While some interlinkages were detected at both scales, others were only identified at the more granular national scale. The various positive and negative relationships identified between the state of the ecosystem, direct drivers of change, state of human well-being, and socioeconomic and environmental factors highlight the importance of considering the impact of indirectly related factors. While some impacting factors are common in global and national settings, identifying other national factors considered to have synergies or trade-offs with waterrelated ecosystems is imperative to inform the development of targeted policies and interventions to protect these ecosystems.
The analysis identified strong interlinkages related to policies that integrate land and water conservation, ensure suitable water infrastructure in urban areas, provide mitigation of pollution and address impacts from water withdrawals associated with economic activity. The analysis revealed mostly examples of relationships consistent with published evidence and intuition. For example, population living in urban areas was found to be positively interlinked to a decline in marine-related ecosystem indicators, confirming the impact of effluents from large cities on the eutrophication of coastal areas.
The inclusion of global and national levels in the statistical analysis provided an opportunity to verify global interlinkages with national case studies and highlight the impact of data disaggregation. For instance, conservation efforts were consistently positively interlinked with freshwater-related ecosystem indicators at both levels, while water-use efficiency indicators were interlinked with freshwater-related ecosystems only at the national level.
The analytical approach has exposed some of the critical data gaps in water-related ecosystems and has challenged the suitability of some indicators to detect meaningful change in the health of freshwater- and marine-related ecosystems. The freshwater-related ecosystem assessment was limited to interlinkages between various metrics of the area of freshwater in each country. Similarly, the lack of disaggregated catchmentlevel data constrained the ability to meaningfully assess coastal ecosystems. While these data sets benefit from the ability to provide consistent measurement using remote sensing across the globe, they are limited in their ability to measure the water quality, volumes or ecosystem health of waterbodies. There may be opportunities to further utilize citizen science, satellite imagery, low-cost in situ monitoring and big data to produce measures of water quality and/or volume within various waterbodies.
It is critical that the successes of the SDG indicator framework be translated into disaggregated data capable of informing subnational policies while maintaining compatibility at a global scale. Data and indicators are key for informed decision-making and policy design to know how realistic options are, what inconsistencies might result from decisions, how the cost of such inconsistencies can be mitigated and how trade-offs can be explained. Considering that most environmental policies, including water policies, are developed at the national or subnational scale, disaggregated data is needed to inform policy.
Re-evaluating the suitability of the current indicator methodologies to parse true change in the environment from data and methodological artefacts is needed to bolster data collection for other environment-related indicators. Moreover, the analysis revealed the importance of incorporating more ecologically relevant spatial groupings. Catchment-based or ecosystembased aggregations may provide more insight into the ecological dimension of many of the interlinkages identified for freshwaterand marine-related ecosystems. However, methods and tools used are expected to be developed concomitantly to facilitate actionable use of data by policymakers working within political or geographical boundaries.
A fuller understanding of SDG interlinkages will ultimately allow for the design of more effective policy responses. For example, integrated water resources management is an optimal policy response that requires the incorporation of scientific analysis of the most relevant external drivers of ecosystem and resource issues, a comprehensive planning approach as well as a traditional approach that focuses on stakeholder input. This is critical to achieving policy coherence and recommendations that are both policy relevant and scientifically defensible.
Sustainable development and the 2030 Agenda can only be achieved through an all-sectoral approach that integrates environment-related indicator trends with robust policy analyses. Its interlinked nature calls for policy coherence for sustainable development through an integrated approach to ensure the production of complementary policies and the avoidance of tradeoffs.