Accelerating progress on tackling the climate crisis through data collaboration

Should research institutions from developing countries provide open access to their data? Who would benefit? These are highly relevant questions for environmental research. In the backdrop of increasing discussions regarding open data, attention should be paid to guarantee that research institutions from developing countries do not become mere data providers. At the same time, restricted access to data should not become a barrier for advancing our knowledge of critical environmental issues. Open access policies for environmental data must be developed not only for advancing science, but also to provide growing opportunities for researchers in developing countries.

Australia invests significant resources in environmental data acquisition, management and publication. Although data are abundant, users are typically hampered by an inability to discover, access and re-use the data. The National Environmental Information Infrastructure (NEII) activity will improve the effectiveness and efficiency of discovering, accessing and re-using environmental data beyond its primary purpose. The NEII is envisioned as a federation of environmental data nodes adopting common standards to create a data infrastructure with an initial focus on nationally significant environmental data. Its primary focus is on the discovery and re-use of national environmental data that is already well-managed, but that currently has limited application beyond its original business purpose. In common with spatial data infrastructures, the NEII encompasses common data models, exchange formats and standard network protocols along with centralised catalogues of uniform metadata descriptions. It also includes standardised models for describing environmental measurements, monitoring sites and methods used to observe the environment to address the unique requirements of environmental information. These architectural elements are more fully described in the NEII Reference Architecture (Bureau of Meteorology, 2014b, www.neii.gov.au). The Bureau of Meteorology is leading the development of the NEII including provision of core coordination and integration infrastructure, as well as the governance and collaboration framework for its development and operation. However its ongoing success is dependent on establishing enduring partnerships with major national environmental information organisations. The NEII Programme is framed around five focus areas including (a) Communication, (b) Engagement, (c) Policies and frameworks, (d) Data management, and (e) ICT build. This paper provides a more detailed overview of one component of the Data Management area regarding the development of the NEII Conformance Framework. Conformance means following established guidelines, specifications and standards or working towards them. A conformance framework is a structured set of guidelines that detail the levels at which a participant provides services that comply with the NEII architecture. The NEII Conformance Framework has been developed to (a) enable partners to set priorities against specific expectations that will jointly lead to developing a sustainable federated environmental information system; (b) enable users of the NEII to rapidly assess the suitability of data for their business needs; and (c) provide a common measurement system to monitor progress in the development of the NEII. The framework adopts a capability maturity model approach to describe the steady improvement required for NEII nodes to progress from a basic data release (experimental data service with limited operational support) to an enduring federated system providing environmental information. The design of the model is informed by other maturity-based approaches for open data such as the Open Data Institute's certification approach (Open Data Institute 2015). The paper also presents a worked example of the conformance framework against the Bureau of Meteorology's Australian Hydrologic Geospatial Fabric NEII data services. The paper concludes with an overview of major learnings from the programme to-date. These relate primarily to reducing the cost of participation in NEII by data custodians given the challenge of delivering data using NEII standards; and developing approaches to better support users to use the portfolio of NEII data given some of the methods of data delivery may be new to non-technical users. The NEII programme has prioritised both these and developed work packages to achieve improvements.

In spring 2020, the Open Data Institute (ODI) published a report summarising the 1 results of an initial investigation into the sustainability of data institutions.Data institutions are organisations whose purpose involves stewarding data -that is, collecting, maintaining and sharing data -on behalf of others, often towards public, educational or charitable aims.The sustainability of these organisations is vital to ensure that they can continue to deliver on their mission, and drive social, economic and environmental impact.But we found that achieving financial sustainability is often challenging for data institutions as some of the possible revenue models can hinder their goal of increasing access to data.This report is the result of our continued research into the business models of data institutions and the surrounding funding landscape.We have further explored the revenue models used by data institutions by looking at a broader set of data institutions through our desk research.We then focused on examining the cost structures of a small number of data institutions and the approaches they have taken to lower costs so that they do not have to generate as much revenue.We looked at data institutions that are relatively young as well as others that have been operational for a long time.Finally, we explored what drives public and philanthropic funders' decisions to fund data institutions.We found further validation that a mix of revenue sources, while not always feasible or indeed necessary, seems to be common among 'successful' data institutions.We also found that a mixed revenue model can help distribute the costs of running the data institution more equitably among the stakeholders that gain value from the data institution's work.Some revenue streams, such as membership and subscription fees, seem to be used only when data institutions play particular roles while others, such as grants, appear to be common across different roles.Understanding the value of data institutions for key actors in the ecosystem that surrounds them -for data contributors, data users, decision makers and funders -may help to identify the revenue models available to data institutions.Continuing to draw on the Business Model Canvas developed by Alexander Osterwalder, we examined the key activities and key resources that drive data institutions' costs.Staff costs appear to make up the majority of costs for data institutions, driven by activities such as technical infrastructure development and maintenance; data collection, curation and maintenance; strategy development; governance; and community engagement.The strategies to reduce costs come either from the choice of services used -for example, choice of data hosting service -or from the design of the data institution and its services.Most strategies to reduce costs come with risks that need to be balanced.Some strategies -say, adopting a crowd-sourcing model for datamay reduce costs in some areas (for example data collection) but raise it in other 1 Open Data Institute (2020), 'Designing sustainable data institutions', https://theodi.org/article/designing-sustainable-data-institutions-paper/Open Data Institute 2020 / Report Data institutions: reducing costs and improving sustainability 3Providers Running a Red Queen's Race?', https://educopia.org/red-queens-race/Open Data Institute 2020 / Report Data institutions: reducing costs and improving sustainability 4Recommendations for those funding or supporting data institutions Identify opportunities to align your approach with other funders Support data institutions to grow beyond their core activities Clearly articulate your approach to funding data institutions Open Data Institute 2020 / Report Data institutions: reducing costs and improving sustainability 5 6 recovery from COVID-19.Over time we think that data access initiatives might lead to new data institutions being created, or existing institutions will have a strengthened role as stewards of 3 The Open Data Institute (2020), 'What do we mean by data institutions?', https://theodi.org/article/what-do-we-mean-by-data-institutions/tps://theodi.org/article/what-do-we-mean-by-data-institutions/4 The Open Data Institute (2020), 'What do we mean by data access initiatives?', https://theodi.org/article/what-do-we-mean-by-data-access-initiatives/tps://theodi.org/article/what-do-we-mean-by-data-access-initiatives/5 Open Active (n.d.), https://www.openactive.io/ 6Emergent Alliance (n.d.), https://emergentalliance.org/

As urban populations burgeon globally, the imperative to foster sustainable cities becomes increasingly pressing. One primary challenge in urban sustainability is the fragmented data silos within different stakeholders. Addressing these barriers, the key term of the Open Data Ecosystem (ODE) has started to gain a wider appreciation, as it emphasises the need to not only provide free and accessible data assets, but rather a circular, sustainable, demand-driven environment. Towards this perspective, the European-funded project Urban ReLeaf capitalised on the Data Landscape Playbook (DLP) methodology, launched by the Open Data Institute (ODI) to dismantle the data silos in six European cities, i.e., Athens (Greece), Dundee (Scotland), Cascais (Portugal), Mannheim (Germany), Riga (Latvia), and Utrecht (The Netherlands).Four steps of DLP were adopted, called Plays, to examine the objectives of each city, identify the data owners and infrastructure, and assess the ethical context behind data accessibility. For the first play, a three-tier approach was established to (i) evaluate the initial objectives of the cities, (ii) transform them based on the latest perspectives, and (iii) correlate them with the project. Subsequently, the Data Ecosystem Mapping (DEM) was formulated and provided valuable information about the data assets, the data owners and the formal value exchanges between stakeholders that are generating jointly a data source. Continuing, we addressed key aspects related to the data itself. An early outcome of this process was that the majority of pilot cities chose to disseminate their data sources in open-access data repositories and machine-usable data formats. Unfortunately, most of the identified datasets were an outcome of individual data collection campaigns revealing any intention to continue.Through the fourth step, we investigated the ethical content following FAIR guidelines. Each data source was classified according to ODI’s Data spectrum scheme (i.e., Closed, Shared, Open) and thus identified the tendency of the European cities towards open access policies. The latest was verified through the identification of the open-accessed data dashboards and licences. An exemption from the general adoption of the Creative Common (CC) licenses was Mannheim, which established the tailored dl-de-by-2.0 license of Germany. Finally, a preliminary review was applied towards the trustworthiness of the released data, investigating methodological procedures that safeguarded the inner trust of data, or the outer trust by the requested public’s opinion.In conclusion, the integration of the ODI-DLP in urban contexts holds the promise of breaking down data silos, fostering circularity, collaboration, and propelling cities towards sustainability. By investigating the existing open data principles, and interoperable technologies that are used and engaging citizens, cities could harness the full potential of their data to inform policies and initiatives that enhance the Quality of Life (QoL) for residents and pave the way for a more sustainable urban future.Acknowledgement: This research has been funded by the European Union’s Horizon Europe Research and Innovation Programme under Urban ReLeaf project (Grant Agreement No 101086638).

<p>Agricultural use of pesticides helps control a range of pests and diseases that threaten crops, thereby avoiding yield losses and improving the quality of the food produced. However, pesticides applied on agricultural fields dissipate with time. The export of pesticides and their transformation products after application from the agricultural fields threatens the water quality of aquatic systems in many world regions.</p><p>Climate change is further expected to intensify pest pressures and potential pesticide use by affecting agriculture in many ways. Changing climatic conditions can increase pesticide leaching due to increased and frequent rainfall, higher degradation rates, or higher temperatures or soil moisture contents. The indirect effects are changes in land use, the timing of crop cultivation, selection of other crop types, new pests and changed pest behaviour, etc. Additionally, several socio-economic factors influence pesticide use at the farm and national level, including regulation and legislation, economy, technology and crop characteristics. In order to better understand the pesticide risk to surface waters in the future, we aim to understand the influence of both climate and socio-economic change on pesticide use and fate.</p><p>Various catchment-scale models are available to assess pesticides and their impacts on water bodies. However, most modelling approaches solely concentrate on the total amount or concentration of pesticide exported from a catchment and do not necessarily analyse the future change of pesticide and transformation products. We propose an integrated modelling framework to answer the research questions: What are the current significant climate and socio-economic drivers influencing pesticide use and emissions? How can climate change influence pesticide and transformation products emission trends? How will socio-economic change influence pesticide emissions?</p><p>The integrated modelling framework helps to include adapting agricultural production to climatic (e.g., temperature, precipitation) and socio-economic drivers (e.g., land use, crop type, pesticide regulation) and quantifying pesticide emissions with the Zin-AgriTRA pesticide fate model. The ZIN-AgriTra is a catchment scale reactive transport model which can simulate agrochemical and transformation products exported from agricultural catchments. We use the Eur-Agri-SSP scenarios that extend and enrich the basic Shared Socio-economic Pathways with a regional and sectoral component on agriculture to explain the socio-economic change and climate projections for Representative concentration pathways to adopt climate change scenarios.</p><p>The integrated modelling framework links the future scenario results from independent, standalone models that present crop rotation, land use, pesticide regulation and climate to the pesticide fate model (Zin-AgriTRA). The framework is applied to an agricultural catchment in Burgenland, Austria, to quantify pesticide pollution under future climate and socio-economic change up to 2050.</p><p> </p>

We assessed the effects of historical (1931–1998) changes in both land use and climate on the water budget of a rapidly urbanizing watershed, Ipswich River basin (IRB), in northeastern Massachusetts. Water diversions and extremely low flow during summer are major issues in the IRB. Our study centers on a detailed analysis of diversions and a combined empirical/modeling treatment of evapotranspiration (ET) response to changes in climate and land use. A detailed accounting of diversions showed that net diversions increased due to increases in water withdrawals (primarily groundwater pumping) and export of sewage. Net diversions constitute a major component of runoff (20% of streamflow). Using a combination of empirical analysis and physically based modeling, we related an increase in precipitation (2.7 mm/yr) and changes in other climate variables to an increase in ET (1.7 mm/yr). Simulations with a physically based water‐balance model showed that the increase in ET could be attributed entirely to a change in climate, while the effect of land use change was negligible. The land use change effect was different from ET and runoff trends commonly associated with urbanization. We generalized these and other findings to predict future streamflow using climate change scenarios. Our study could serve as a framework for studying suburban watersheds, being the first study of a suburban watershed that addresses long‐term effects of changes in both land use and climate, and accounts for diversions and other unique aspects of suburban hydrology.

Mountain regions are globally important areas for biodiversity but are subject to multiple human‐induced threats, including climate change, which has been more severe at higher elevations. We reviewed evidence for impacts of climate change on Holarctic mountain bird populations in terms of physiology, phenology, trophic interactions, demography and observed and projected distribution shifts, including effects of other factors that interact with climate change. We developed an objective classification of high‐elevation, mountain specialist and generalist species, based on the proportion of their breeding range occurring in mountain regions. Our review found evidence of responses of mountain bird populations to climate (extreme weather events, temperature, rainfall and snow) and environmental (i.e. land use) change, but we know little about either the underlying mechanisms or the synergistic effects of climate and land use. Long‐term studies assessing reproductive success or survival of mountain birds in relation to climate change were rare. Few studies have considered shifts in elevational distribution over time and a meta‐analysis did not find a consistent direction in elevation change. A meta‐analysis carried out on future projections of distribution shifts suggested that birds whose breeding distributions are largely restricted to mountains are likely to be more negatively impacted than other species. Adaptation responses to climate change rely mostly on managing and extending current protected areas for both species already present, and for expected colonizing species that are losing habitat and climate space at lower elevation. However, developing effective management actions requires an improvement in the current knowledge of mountain species ecology, in the quality of climate data and in understanding the role of interacting factors. Furthermore, the evidence was mostly based on widespread species rather than mountain specialists. Scientists should provide valuable tools to assess the status of mountain birds, for example through the development of a mountain bird population index, and policy‐makers should influence legislation to develop efficient agri‐environment schemes and forestry practices for mountain birds, as well as to regulate leisure activities at higher elevations.

Climate and land use change are among the main drivers affecting virtually all species on earth. There were extensive studies projecting impacts of climate and land use changes on habitat loss and fragmentation but few on connectivity loss, and those that investigated connectivity did not disentangle the combined effects between climate change and land use change. This study uses the Himalayan brown bear (Ursus arctos isabellinus) as a case study to illustrate an approach for disentangling the effects of climate change and human land use on population connectivity in the future. First, we assessed the current spatial pattern of population connectivity by simulating cumulative resistant kernels and factorial least‐cost paths with empirical field data. Then, we simulated the changes in connectivity due to future climate change under alternative emission scenarios (RCP 2.6 and RCP 8.5) in mid (2041–2060) and late (2061–2080) 21st century, which served as baseline scenarios of future connectivity. Finally, we estimated the changes in future connectivity due to human land use changes by adding a low and a high human land use activity scenario to the baseline climate change scenarios in our simulations. Alarmingly, all high emission scenarios, with or without human land use change, were projected to result in >99% reduction in current core areas of connectivity by the end of 21st century at 50th percentile threshold or above. This study demonstrates a spatially explicit scenario modeling approach to examine the interplay between future climate change and human land use on species connectivity. Our results suggest that regional land use regulations may be insufficient to conserve connectivity for HBB if nothing is done to reduce climate change at a global scale.

Groundwater contributes a significant proportion of stream flow, and its contribution varies temporally throughout the year. The objective of this study was to investigate the temporal dynamics of groundwater contribution to stream flow under the effects of climate and land use changes. A study area of the Mainstem sub-watershed of the Kiskatinaw River watershed, British Columbia, Canada was used as a case study. A physically conceptual model, Gridded Surface Subsurface Hydrologic Analysis (GSSHA), was developed for the study area. One greenhouse gas (GHG) emission scenario (i.e., B1: more integrated and environmental friendly world) was used for climate change study for 2012-2016, and land use changes scenarios were generated for shortterm period (2012-2016) due to limited future projected land use data. The simulation results revealed that climate change affects significantly the temporal patterns of mean groundwater contribution to stream flow. Due to precipitation variability, these contributions varied monthly, seasonally, and annually. When land use changes (i.e., increasing forest clear cut area, and decreasing forest and agricultural areas) were combined with climate change scenarios, these contributions were decreased due to changes in the flow patterns to the regime with more surface runoff and stream flow but less groundwater discharge. Compared to the reference period (20072011), the mean annual groundwater contribution to stream flow from 2012 to 2016 under the B1 climate change scenario and the combined effects of B1 scenario and land use changes is expected to decrease by 1.8% and 4.3%, respectively, due to increased precipitation (on average 3.6% under the B1 scenario) and temperature (on average 0.36°C under the B1 scenario), and land use changes. The results obtained from this study will provide useful information for seasonal and annual water extractions from the river and allocation to the stakeholders for future water Original Research Article Saha; BJECC, 5(1): 1-22, 2015; Article no.BJECC.2015.001 2 supply, as well as ecological conditions of the stream, which will be beneficial to aquatic ecosystems. They will also provide how land use changes can impact the groundwater contribution to stream flow, which will be useful for planning of water resources management considering future climate and land use changes.

Addressing the policy challenges and opportunities of “Big data”

This GIS study examines how climate change affects drought risk in semi-arid regions. Semi-arid regions are especially vulnerable to droughts due to rising global temperatures and changing precipitation patterns. The study analyses drought trends using diverse dataset, including climate projections, geographical information, and environmental parameters. The study incorporated temperature, precipitation, and moisture data, alongside land cover, soil type, and land usage information. Sabarmati River Basin residents and ecosystems need drought management and adaptation. We studied climate change using temperature, precipitation, and moisture data. Plant cover, soil type, and land usage indicated regional drought. GIS, meteorological, and environmental data projected drought. Drought risk model includes LULC, VCI, LST, SMI, and NDDI by integrating all parameters such as LULC, NDVI, Rainfall, etc. all meteorological and environmental data using GIS. Climate and land use changes increased drought risk in the study region. GIS-based drought indices and risk assessment models quantify and map drought intensity, frequency, and duration under different climate change scenarios. The results help semi-arid regions develop adaptive drought risk management strategies. This interdisciplinary approach emphasises the ability of GIS for assessing and mitigating climate-induced drought occurrences.

The impact of land use- and climate change on the managed eco-geomorphic balance in the Alps

Fossil-fuel based energy use in agriculture leads to CO2 and non-CO2 emissions. We focus on emissions generated within the farm gate for crop and livestock production and from fisheries, providing information relative to the period 1970–2019 for both energy use and the associated greenhouse gas (GHG) emissions. Country-level information is generated from UNSD and IEA data on energy in agriculture, forestry and fishing, relative to use of: gas/diesel oil, motor gasoline, liquefied petroleum gas (LPG), natural gas, fuel oil and coal. Electricity used within the farm gate is also quantified, while recognizing that the associated emissions are generated elsewhere. We find that in 2019, annual emissions from energy use in agriculture were about 523 million tonnes (Mt CO2eq yr−1), and up to 1,029 Mt CO2eq yr−1 when including electricity. They increased 7 % since 1990. The largest emission increases from on-farm fuel combustion were from LPG (32 %), whereas significant decreases were observed for coal (−55 %), natural gas (−50 %), motor gasoline (−42 %) and fuel oil (−37 %). Conversely, use of electricity and the associated indirect emissions increased three-fold over the 1990–2019 period, thus becoming the largest emission source from energy use in agriculture since 2005. Overall the global trends were a result of counterbalancing effects: marked decreases in developed countries in 2019 compared to 1990 (−273 Mt CO2eq yr−1) were masked by slightly larger increases in developing and emerging economies (+339 Mt CO2 eq yr−1). The information used in this work is available as open data at: https://zenodo.org/record/5153241 (Tubiello and Pan, 2021). The relevant FAOSTAT (FAO, 2021) emissions database is maintained and updated annually by FAO.

Agricultural direct energy use is responsible for about 1–2% of global emissions and is the major emitting sector for methane (2.9 GtCO2eq y−1) and nitrous oxide (2.3 GtCO2eq y−1). In the last century, farm mechanisation has brought higher productivity levels and lower land demands at the expense of an increase in fossil energy and agrochemicals use. The expected increase in certain food and bioenergy crops and the uncertain mitigation options available for non-CO2 emissions make of vital importance the assessment of the use of energy and the related emissions attributable to this sector. The aim of this paper is to present a simulation framework able to forecast energy demand, technological diffusion, required investment and land use change of specific agricultural crops. MUSE-Ag & LU, a novel energy systems-oriented agricultural and land use model, has been used for this purpose. As case study, four main crops (maize, soybean, wheat and rice) have been modelled in mainland China. Besides conventional direct energy use, the model considers inputs such as fertiliser and labour demand. Outputs suggest that the modernisation of agricultural processes in China could have the capacity to reduce by 2050 on-farm emissions intensity from 0.024 to 0.016 GtCO2eq PJcrop−1 (−35.6%), requiring a necessary total investment of approximately 319.4 billion 2017$US.

Given the severity of future climate change projections and associated risks to human and natural systems, societies are now faced with a strong imperative to develop adaptation policies and actions in response. Spatial planning, which is the process through which the development and use of land is visualised, negotiated and regulated, has an important role to play in adapting to the changing climate. Despite positive steps forward in some locations, there remains a gap between spatial planning’s potential capacity to support the achievement of adaptation goals and the realisation of this role in practice. This paper reports on the findings of an online Delphi survey undertaken to build understanding of the relationship between spatial planning and climate change adaptation. The survey secured the input of over 70 academics, planners and policy makers working across these fields in ten different countries. Its results offer insights on barriers inhibiting spatial planning’s contribution to adaptation, which range from overarching systemic issues through to those concerning the detailed workings of the planning system. The Delphi survey also identified solutions that could help build the capacity of spatial planning to progress the adaptation agenda. Approaches include enhancing the adaptation knowledge, skills and technical capacity of planners and applying different concepts and methods to align spatial planning more closely with adaptation goals. In presenting and analysing the results of the Delphi survey, the aim of this paper is to help build the capacity of policy makers, practitioners and researchers to adapt spaces and places for the changing climate.