Advertisement

  • Loading metrics

Open Access

Essay

Open science practices for better NDCs: Supporting transparent and accountable climate mitigation action

Abstract

The Nationally Determined Contributions (NDCs) are key documents providing the starting point for increasingly ambitious ‘pledge-and-review’ cycles leading to the implementation of the Paris Agreement. However, current NDCs often lack consistent and transparent targets and indicators to ensure their in-country realisation and their monitoring, tracking, and reporting in compliance with the current climate change regime. This is particularly true for developing countries, where resources and capacity to define and implement climate action plans are still largely lacking among practitioners. To tackle this problem, this essay proposes a framework for NDCs development focusing on the practitioners’ – i.e., modellers and expert analysts – uptake of open science practices, particularly concerning open-source tools and open data, via capacity-building initiatives as a first step towards transparent and accountable NDCs delivery and implementation to the benefit of national and international climate governance. The framework is applied to the case study of Costa Rica, to test its relevance and applicability.

Citation: Beltramo A, Henrysson M, Usher W (2026) Open science practices for better NDCs: Supporting transparent and accountable climate mitigation action. PLOS Clim 5(2): e0000839. https://doi.org/10.1371/journal.pclm.0000839

Editor: Alessandro Del Ponte, The University of Alabama, UNITED STATES OF AMERICA

Published: February 12, 2026

Copyright: © 2026 Beltramo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Funding: This work was supported by the Climate Compatible Growth (CCG) programme, funded by the UK’s Foreign, Commonwealth and Development Office (grant nr. GB-GOV-1-300125 to AB and WU). However, the views expressed herein do not necessarily reflect the UK government’s official policies. The funder had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Following the 2015 adoption of the Paris Agreement, climate change governance is now focusing on implementation for effective climate action [1]. The ‘pledge-and-review’ process defined in the Paris Agreement binds the signatory Parties to the Agreement to submit their Nationally Determined Contributions (NDCs) to the secretariat of the United Nations Framework Convention on Climate Change (UNFCCC). This serves as the first step to outline the Parties’ climate change mitigation targets and actions to reduce greenhouse gas (GHG) emissions over 5-year ambition cycles [2–4].

In this essay, we adopt an agnostic stance in examining the implementation mechanisms envisaged in the Paris Agreement from the perspective of practitioners, such as system modellers and expert analysts, informing and supporting the preparation of the NDCs. We investigate the relevance and applicability of open science practices for the practitioners’ work, focusing on the use of open-source tools and open data for integrated energy, climate, and resource systems modelling, to support transparent, accountable, and sustainable climate mitigation efforts.

The NDCs are intended to serve both technical and political purposes. Despite acknowledging both bodies of literature, in this essay we will focus primarily on the technical function related to the need for providing quantitative scientific evidence to support climate-related decision-making [5,6]. Following the ‘pledge-and-review’ process outlined in Fig 1, the first round of NDC submissions concluded in 2020, and a new round has now started. Parties need to assess progress and update their plans to ensure compliance with the Paris Agreement goal of limiting the global mean temperature increase to well below 2°C above pre-industrial levels [7,8]. This requires fostering climate change mitigation and mobilising resources to support increasing ambition for effective action [3,7]. This also requires supporting the in-country practitioners with developing or consolidating the skills and expertise needed to assess and plan for transparent and accountable medium- to long-term climate mitigation strategies.

thumbnail
Download:
Fig 1. Schematic representation of the ‘pledge-and-review’ process defined under the Paris Agreement, adapted from [4].

https://doi.org/10.1371/journal.pclm.0000839.g001

The knowledge and resources needed to assess, update, track progress and report on the NDC pledges are lacking particularly in developing countries, where this is reflected by a significant number of countries requesting capacity-building support as a condition for their NDC implementation [9,10]. The literature highlights how the preparation of NDCs particularly, and the subsequent progress-tracking effort required by the Paris Agreement, pose challenges [9,11,12]. The NDC development process is data- and resource-intensive. It requires evidence to be collected and assessed against the climate mitigation goal, using appropriate analytical tools [12,13]. The UNFCCC has outlined basic requirements for the NDC development; however, the process is still left open as to what methodologies should be used to support this effort with a sufficient level of details and systematic processes, that can ensure the evidence provided is complete and coherent across Parties [14]. This affects developing countries’ ability to build and foster relevant expertise and to plan for transparent and accountable climate mitigation actions and goals. It also affects all countries in their ability to compare, review, and assess the pledged goals [11]. Therefore, there is a need to define coherent and systematic methodologies for the development and implementation of transparent, accountable, and therefore credible NDCs and to disseminate the related knowledge and expertise needed, particularly across developing countries [9,12,13].

Open science is gaining recognition as one way to provide wider accessibility to, and transparency and understanding of, scientific knowledge for all, and to create a common base for information and awareness in society. Open science also underscores the need for enhancing collaboration, in favour of accountability and productivity as well as inclusion [15]. In the field of energy and integrated resource systems planning, this is reflected in the ongoing transition from traditional closed, proprietary software and datasets, towards an increasing adoption of open science practices to enhance the accessibility and transparency of data and assumptions underlying model outcomes, towards greater legitimacy and recognition for policymaking [16–18]. These practices include distinctively the development and application of open-source tools, and the publication and adoption of open data, as defined by the UNESCO Recommendation on Open Science [15] (see S1 Text for the complete definitions).

In this essay, we link these two aspects – the need for increasingly transparent and coherent methodologies to develop ambitious NDCs, and the increased recognition and uptake of open science practices to support evidence-based policymaking – to suggest the following. Adopting open science practices, particularly the development and use of open-source models and the collection, collation, release and use of open data for integrated energy, climate and resource systems modelling, would address many of the criticisms and issues identified in the literature on NDC transparency and implementation. This is based on the premise of a willingness to work towards transparent implementation of the Paris Agreement. Furthermore, the adoption of open science practices could facilitate sustainable, i.e., affordable, self-sustaining, capacity-building efforts, particularly in developing countries where they are most needed.

To test this hypothesis, we propose a framework for developing NDCs as part of the Paris Agreement implementation process that embeds open science practices as key elements to enhance a more systematic, transparent, and accountable approach to NDC target setting and implementation. We then demonstrate the relevance of the framework by analysing the Costa Rica NDC development process.

The framework relies on capacity-building, to develop local institutional capacity central to the uptake of open science practices for medium- to long-term planning via robust quantitative analysis. In line with the scope and the implementation mechanisms outlined in the Paris Agreement, the framework provides practitioners with an overview of how the use of open science practices to inform the development of NDCs can enable a more collaborative, transparent, and equitable basis to support climate governance and facilitate each and every step of the ‘pledge-and-review’ process. This essay also highlights and discusses issues of the proposed open approach that are critical for its realisation, drawing on the literature of open science for evidence-based policymaking.

In this essay, we make the following contributions. First, we underscore the relevance and applicability of open science practices in providing a systematic and coherent methodological approach for medium- to long-term planning and the analysis of complex and dynamic systems, which are key to the development of NDCs. To the best of our knowledge, no previous paper has made a direct link between open science practices and their potential contribution to NDCs. Consequently, the framework presented in this paper proposes a way to embed transparency and accountability along the ‘pledge-and-review’ process defined in the Paris Agreement, by leveraging the open science approach. Secondly, we intend to address the need for resources and expertise for NDC preparation particularly in developing countries, by explicitly accounting for capacity-building activities to ensure a common level of knowledge and expertise across all Parties. Finally, we provide practitioners with a comprehensive overview of their potential role and contribution within the broader context of global climate governance, in line with the established recognition of the importance of science- and evidence-based decision-making.

The remainder of the essay is structured as follows. The following background section summarises the evidence underpinning this research, that were gathered from the literature on open science and capacity-building in the context of NDC development and implementation. The proposed framework for effective and accountable NDCs is presented then, followed by the case study of the Costa Rica NDC development process as first example of its implementation. Finally, the essay concludes with reflections on the proposed framework, and suggestions for future work.

2 The need for open science and capacity building for transparent medium- to long-term system planning

Quantitative modelling tools have been used to inform medium- to long-term strategies in energy, climate and other resource sectors since the oil crisis of the 1970s [19,20]. They typically combine social, economic, and technical parameters to represent highly complex systems. For this reason, they are exploratory in nature and cannot be fully validated [21,22]. Nonetheless, they are used to investigate possible evolution pathways of the system under analysis and to support related decision-making processes [16,23,24]. Integrated Assessment Models (IAMs) provide one example of such models, which have been used most prominently by the Intergovernmental Panel on Climate Change (IPCC) to identify potential transition pathways for decarbonisation at the global scale, in a policy-agnostic approach [25,26]. However, energy system models are more commonly used to support policymaking in a more context-specific, geographically detailed scale, such as at the country-level [27].

Some of the signatory Parties to the Paris Agreement already make use of quantitative modelling tools to inform the preparation of their NDCs. This is still not common practice though, and often information about the underlying scientific evidence is missing [13].

Since the early 2000s, open science practices have increasingly developed and become adopted across various disciplines, including long-term planning and modelling. In this context, enhancing the transparency of modelling practices and methods – meaning, enhancing their accessibility and understandability by removing potential barriers to the adoption of the tools, accessibility of the data, and understanding of the insights they provide – is now acknowledged as enabler of societal discussion and the creation of a common understanding of the future, particularly within the context of policymaking [28,29]. Similarly, enhancing the transparency and accessibility of underlying model assumptions and data has proven to support understanding of processes and outcomes, and their questioning by stakeholders such as policymakers and civil society representatives [30]. This well aligns with global climate governance, where transparency is recognised as key contributor to drive climate action and stimulate a higher level of ambition in the commitment to mitigate climate change, despite being still insufficient per se to ensure that the Paris Agreement goals will be met [31,32].

Thanks to its more accessible nature, open modelling provides lower entry barriers to scientifically-sound modelling frameworks and data necessary for planning – by overcoming licensing costs reducing the need for paid training and support services to help with the use of tools and data – as well as additional resources to help new and expert users learn from each other’s experiences and develop new communities to support their efforts [33,34]. For this reason, open modelling offers a fairer and more resource-efficient option for modelling to proprietary or private software and closed data [16,33,35]. This aligns with the recognised need to foster capacity development to enable transparency under the Paris Agreement and to support its implementation. By encouraging international cooperation and support between developed and developing countries, the Paris Agreement emphasises the need for enhancing knowledge and capacity to address climate change where it is most needed [7]. However, capacity-building initiatives thus far have fallen short of meeting the need for more broadly oriented activities that move away from highly complex and detailed reporting mechanisms in favour of simpler, more coherent and systematic accountability practices [31,36,37]. This requires greater emphasis on methods for NDC development, similar to what open models and data can offer, to ensure that the planned targets and actions are credible and ambitious enough [9,12], to generate trust, confidence, and ambition among Parties during the implementation of the Paris Agreement [38,39]. Open modelling appears well suited to provide the systematic, evidence-based approach necessary to deliver on this goal.

Establishing standards and practices for open modelling is key to deliver enhanced transparency in energy and resource systems planning [16,40,41]. Most notably, the FAIR principles for Findable, Accessible, Interoperable and Reusable data provide a set of widely adopted and consistent principles to help guide the process of developing more transparent open-source tools and open data practices, upon which the planning can rely [42]. Contrary to what their name suggests, they target not only conventional data, but also modelling tools, software, and workflow management systems that help with obtaining, processing, and using data, to provide consistent sets of information on open resources to ensure their full transparency, accessibility, reproducibility and reusability [42]. The FAIR principles guide the ongoing development and release under permissive licenses of new open science resources, may they be open-source software, open datasets, or open access literature and knowledge resources to provide practitioners with ever increasing ‘building-blocks’ that aim at facilitating their uptake and application for context-specific analysis.

In summary, improving the accessibility and understandability of open modelling resources and practices offer a unique entry point for delivering science-based systems planning and analysis, like those needed to inform the NDC formulation. The lower entry barriers to open-source tools, open data, and openly accessible training resources also contribute to building, developing, and consolidating the knowledge and capacity needed to enable the open modelling process. This in turn helps with alleviating the financial burden of developing and maintaining the technical knowledge and expertise required to conduct rigorous science-based analysis, particularly in developing country contexts where these capabilities are most needed. Finally, existing open science standards and practices are well suited to help with delivering transparency, accessibility, reproducibility and reusability across new modelling efforts.

3 A framework for effective and accountable NDCs

The NDCs are crucial pledging documents aimed at providing science-based medium- to long-term targets and goals in line with each signatory Party’s implementation of the Paris Agreement, towards the delivery of “effective climate action and related policymaking” [6]. However, developing countries in particular are still struggling with building and maintaining the technical knowledge and expertise needed to inform the NDCs.

To address this issue, we present here a framework that provides a coherent, systematic, science-based methodological approach to the NDC development process. The framework relies on existing open science resources and practices to strengthen the knowledge base and build the capacity needed to define effective and accountable NDCs, while enhancing transparency in the implementation of the ‘pledge-and-review’ process under the Paris Agreement. In the framework presented in Fig 2 we make distinctive use of open-source tools as well as open data to address the knowledge and capacity gaps and needs identified among signatory Parties to the Agreement, thereby informing medium- to long-term systems targets setting, in support of transparent and accountable NDCs.

thumbnail
Download:
Fig 2. Framework for effective and accountable NDCs for climate mitigation pledging, relying on the delivery of open science knowledge and practices via capacity-building to embed transparency along the ‘pledge-and-review’ process of the Paris Agreement.

https://doi.org/10.1371/journal.pclm.0000839.g002

The framework starts from, and is centred around, the transfer of knowledge and expertise via capacity-building initiatives that promote the uptake of open science resources and the implementation of related open science practices. These efforts support the dissemination, adoption, development and release of open-source tools and open data. While primarily developed with focus on the NDC-related efforts, the framework is also designed to mimic the ‘pledge-and-review’ process defined in the Paris Agreement and to highlight the contributions that open-source tools and open data can distinctively provide in each step of the process. In this way, the framework lays the ground to facilitate the technical review of NDCs’ ongoing efforts and progress by relying on the enhanced transparency that open science practices deliver to the analysis. As a result, the framework follows consequential steps that are designed to deliver continuously on the need for knowledge and resources, as well as transparency, effectiveness and accountability of the Paris Agreement implementation process. These steps are intended to take full advantage of the cascading effects provided by both open modelling and open data resources and practices applied consistently and systematically to the development of climate mitigation strategies for NDC target-setting, as well as along the broader ‘pledge-and-review’ process. In this way, open science becomes instrumental in making global climate governance more credible, legitimate, accountable and effective. It also addresses the implementation issues identified by Marion Suiseeya et al. [38] and Bulkeley [43], by fostering trust and support for the process.

In the next sections, we describe the benefits that the dissemination and adoption of open-source tools and open data could bring to each of the steps in the framework represented in Fig 2.

3.1 Step 1: Building knowledge and capacity on open science

The knowledge and capacity-building effort at the top of the framework is designed to deliver on two areas that have remained unaddressed by existing capacity-building initiatives: the need for capacity and expertise to increase transparency of information coming from developing countries, and the need for coherent methods to support mitigation action tracking in line with NDC submissions [39,44].

Capacity-building efforts are made easier by the lower costs and entry barriers delivered by open science, which lower the financial burden embedded in this framework. This translates into facilitating practitioners in learning how to use open modelling frameworks and open datasets made available to use for free, thanks to the removal of high licensing, training, or accessing fees associated with traditionally proprietary resources. One example of this is offered by the training courses available openly as part of the Open Learn Collection developed by the Climate Compatible Growth (CCG) programme [45,46]. The courses are developed and offered by several international organisations together with academic institutions based in developed countries and funded primarily by the UK government. The courses aim at teaching practitioners how to use open-source tools and how to manage and process open data. They target a range of different analytical methods and related modelling tools, including data management, demand forecasting, energy systems and electrification planning. They also provide access to dedicated communities of users, where practitioners that are new to the tools can ask questions and get support benefitting from the free, peer-to-peer approach that open science encourages [47]. This capacity-building approach is also made flexible thanks to open science, enabling each tool to be used and modified to suit the needs of different practitioners and country contexts. It provides access to a variety of tools that can be combined, further developed and customised in their underlying functionalities and equations, in accordance with the respective open license terms.

Another example of a capacity-building effort focussing on the dissemination and application of open-source tools and open data is offered by the in-person trainings coordinated and offered by the RE-INTEGRATE research project [48,49]. This project is funded under the Horizon Europe Research and Innovation Programme, as part of the 2022 call for proposal on Sustainable, secure and competitive energy supply. It targets specifically the delivery of open knowledge and scientific methodologies and tools for energy system modelling to the African continent, as contribution to the long-term partnership between the African Union (AU) and the European Union (EU). In practice, it supports the development and sharing of energy planning capacity that includes open-source tools, open datasets, open teaching material and open science practices between developed countries in the EU to developing countries in the AU [50,51]. Most notably, it does so by building on the teaching material offered by the CCG Open Learn Collection, thus illustrating a perfect example of how the open nature of the resources utilised across these research projects allows for a more efficient use of resources and a wider dissemination impact, in favour of a lower financial burden.

Open-source tools and open data are instrumental to quantitatively assess national resource systems and analyse medium- to long-term pathways for reducing GHG emissions, following scientific methods and analytical approach. They offer a wide range of tools and resources to best analyse context-specific issues and sectors, depending on each country interest and focus. Thanks to their open nature, open-source tools are also designed to facilitate the users in investigating and understanding the underlying scientific methodologies and assumptions embedded in each open-source software or code. Open data are designed to provide free access to information and data pertaining to the configuration and status of a system, and to allow for the data to be used and reshared as the basis for developing scientific analysis in accordance with their open licensing conditions. This contributes naturally to enhancing the transparency and accessibility of the software or code, as well as the underlying data used to characterise a distinctive model, to both practitioners – such as the modellers and expert analyst that this paper intends to address – and other stakeholders – like policymakers or representatives of civil society – who might be interested or involved in the process. This takes advantage of the cascading effect of open science qualities that ultimately allow also for scrutiny and improvement via collaborative efforts across society.

3.2 Step 2: Defining the NDC pledges

Following the attendance of dedicated online or in-person trainings on the use of open-source tools and open datasets, practitioners can apply the acquired knowledge and skills to use and develop tools (methods, models) and resources (data) for planning climate mitigation pledges, in accordance with their context-specific needs. Thanks to their open nature, open-source tools facilitate the implementation of scientific methods to underpin the analysis. Similarly, open data facilitate the use of up-to-date information to correctly characterise the system under analysis. In addition, the open nature of this planning effort allows practitioners to engage with stakeholders, as well as to customise the tools and methods and to process data so that they can be adapted to address context-relevant needs. This allows them to produce transparent evidence-based climate mitigation plans and define strategic targets to underpin and justify the pledges made in the NDCs, thus enhancing their credibility, effectiveness, and accountability along the process.

3.2.1 Open-source tools.

Open-source tools – highlighted in red in the framework (Fig 2) – such as software, codes, or workflows, developed following existing open standards and principles contribute to enhancing the credibility and effectiveness of the process, by providing an open and transparent methodology that can be questioned, adapted, and improved to develop relevant evidence-based analysis. This means, for instance, that each piece of code is also associated with a documentation, detailing the underlying equations and assumptions embedded in the software or code in question so that it can be understood by humans. It also means that each piece of code is associated with an open license, ensuring that the code can be legally accessed and scrutinized when needed. The code should also make use of automated distributed version control systems such as Git™ [52] and the related GitHub [53] developer platform, which facilitate collaboration across practitioners and communities of users, support documentation efforts, and enable tracing errors along the way. This approach allows for an increasingly collaborative effort, by providing the tools and resources for documenting the process, by allowing a wide range of practitioners to contribute to the efforts, and by facilitating stakeholders’ access to and scrutiny of the process.

Open-source tools rely on data to be able to characterise models, perform analysis, develop relevant scenarios, and provide evidence-based insights.

3.2.2 Open data.

Open data – highlighted in orange in the framework (Fig 2) – contributes to the transparency and accountability of the process, by providing open, traceable, and verifiable data that documents and informs on the status of the system under analysis, in line with the FAIR data principles. This means, for instance, that each dataset used in the analysis is provided with an open license that makes it openly available to be re-used and re-shared with other interested stakeholders. In addition, open data is provided with a consistent set of metadata that can be both human and machine-readable, so that information pertaining its original data source, how the original data is processed to be used as input to the model used for the analysis, and the licensing conditions under which the dataset can and has been accessed, are provided to other practitioners and stakeholders involved in the process. To provide machine-readable metadata also means to allow for this open information to be automatically processed by a computer, thus facilitating its re-use and accessibility across different software and reporting platforms. The provision of metadata and an open license associated with the open datasets used and generated in the models therefore allow for key information about the data to be systematically compiled and reported to the UNFCCC, in accordance with existing reporting guidelines [54,55]. This ultimately helps reducing the need for human resources and lowering the risk for human error associated with compiling this information in the descriptive format currently recommended by the UNFCCC [56].

Making use of open data, together with open resources for workflow management and data processing, to characterise open-source models ensures accountability by enabling models’ inputs and assumptions to be independently examined, verified, or contested if needed.

3.3 Step 3: Monitoring and tracking progress in the NDC implementation

Once the NDC targets and pledges have been defined and submitted to the UNFCCC, and a plan for their implementation is in place, it is time to follow along the implementation to ensure it comes to reality. Progress in this phase can be tracked and monitored via systematic collection and update of the same information and data that were used in the planning phase. Here, practitioners can make use of established open data practices during the collection and update of existing datasets by assigning consistent human and machine-readable metadata, in line with the FAIR data principles, and releasing the updated datasets under permissive open licenses. This open approach to data collection and release provides again a more transparent and consistent basis for informing the track and review process of ongoing activities. It results in a set of accessible and coherent information that can be compared across submissions, and later used as a starting point for the preparation of the following round of NDCs. In this way, a virtuous circle can be generated that learns from past experiences and enables increased ambition towards the realisation of the Paris Agreement, in line with the scope of the NDCs.

3.4 Step 4: Assessing and reporting on climate action

The new set of open data generated in the step 3 of the process is now ready to be compared and assessed against the targets and pledges of the original NDC submission. The consistent reporting and updating of relevant open datasets can now be seamlessly integrated by practitioners into both the Biennial Transparency Reporting (BTR) and the Global Stocktake exercise. Particularly thanks to the adoption of consistent open licenses and machine-readable metadata, the information can be read in, processed, and compared across datasets to provide a detailed assessment of the progress in the NDC implementation along the years. This facilitates the ultimate reporting effort and helps enhance the legitimacy of the entire process by providing evidence of progress (or lack thereof) along the ‘pledge-and-review’ cycle.

3.5 Step 5: Reviewing the implementation of NDC pledges

Once the reporting effort in step 4 is completed, the technical review begins. Practitioners are specially trained now to analyse the information and data provided by the BTR and Global Stocktake efforts and to scrutinise the current progress towards the climate mitigation goal. Thanks to their involvement in the model preparation and data collection efforts throughout each step of the framework, the practitioners are now equipped with the knowledge and skills needed to review the information submitted by the different Parties. This facilitates the comparison and review efforts at the UNFCCC level, thereby supporting the global climate governance process.

The legitimacy of climate actions can now be established by all Parties through a thorough review of information provided in the form of open datasets along the NDC implementation process. This review supports the documentation of ongoing progress and the evaluation of the impact of planned measures on the climate. At the same time, the outcome of this effort provides new, carefully reviewed information and data that can be input into the updated modelling and planning activities that inform the new NDC submission and mark the start of a new implementation cycle.

4 Open science application for locally-owned transparent and accountable NDCs: The case of Costa Rica

We analyse here the case of the NDC and the related National Decarbonization Plan (NDP) of Costa Rica [57,58] to illustrate the first example of how the proposed framework can be implemented to support more robust climate mitigation planning and action.

Starting in 2016, Costa Rica developed in-country open modelling capacity to build expertise and explore science-based decarbonisation pathways in support of policymaking. This process began with Costa Rican researchers participating in capacity-building events, such as The Summer School on Modelling Tools for Sustainable Development – OpTIMUS, which focus on the dissemination and application of open modelling tools and are financially supported by international organisations such as the World Bank and the United Nations Department of Economic and Social Affairs [59]. The training served as the starting point for Costa Rican modellers to develop an energy system model of Costa Rica, making use of the open-source energy modelling system framework OSeMOSYS [60]. As a result, in 2019 the OSeMOSYS-CR model was developed [61,62]. This country-specific model is built using the OSeMOSYS modelling framework, incorporating national open datasets available for Costa Rica. In addition, the local researchers made use of the open nature of these resources to customise the modelling software, develop model-specific workflow management systems, and create an online model documentation. This effort ensured that the evidence generated by the country-specific model was made “retrievable, reusable, and repeatable” through to the adoption and implementation of open science resources and practices [63,64]. Taking full advantage of the modular structure of the OSeMOSYS modelling framework, the OSeMOSYS-CR model represents both energy and transport sectors [62]. In addition, the model has recently been further expanded to include land and water resource systems as part of the newly developed CLEW-CR model. This model extension is based on the Climate Land Energy and Water systems (CLEWs) framework [65–67], and it takes into account also the impact of the energy sector on other resource systems, towards a more comprehensive sustainability assessment [68].

Due to its open nature, the OSeMOSYS modelling framework allowed for the flexibility and customisation needed to better represent the Costa Rica energy sector, as defined in the OSeMOSYS-CR model. The absence of proprietary software licenses and maintenance costs facilitated its use across several institutions in Costa Rica. Furthermore, the open release on the software development platform GitHub of the customised OSeMOSYS-CR model code under MIT license, together with the model data under CC-BY 4.0 licence, enables the wider dissemination and uptake of the model by new researchers and analysts [61,63,69]. The source of the data associated with the model is also openly accessible through the online data portal of the Sistema Nacional de Métrica del Cambio Climático (SINAMECC). This portal facilitates systematic data gathering, monitoring, and reporting of climate change metrics across institutions at the national level, thereby contributing to the Measurement, Reporting and Verification (MRV) process under the Paris Agreement [70,71]. The combination of open-source modelling and open data, together with a detailed model-specific documentation, allowed for participatory stakeholder engagement in scenario development and for higher accountability of results based on the model outcomes. This contributed to enhance the transparency of the process and enable engagement with key stakeholders to better account for local priorities and interests in identifying suitable pathways for mitigating climate change [63,69,72].

The open modelling process that took place in Costa Rica is explicitly referenced in the country’s current NDC submission, where the OSeMOSYS-CR and the CLEWs-CR open models are mentioned as key elements of the Modelo Integrado de Trayectorias de Descarbonización de Costa Rica (CR-DPIM), together with a clear commitment to open data collection and distribution via the SINAMECC data portal [57,73]. This showcases a coherent methodological approach undertaken by the Costa Rica government, based on the adoption of open science practices to provide transparent, quantifiable evidence to underpin Costa Rica’s pledges and commitments to mitigating climate change.

5 Reflections and conclusions

The Paris Agreement is struggling to deliver effective climate change mitigation action. The NDCs are key documents that can help decision-makers identify effective science-based strategies and accountable targets defining each Party’s contribution to mitigate climate change. However, the knowledge and capacities needed to tap into existing analytical methods and scientific evidence, and to deliver effective and accountable strategies, are lacking, particularly in developing countries. This affects the ability of the Parties to identify realistic and accountable targets and to monitor track and report on their implementation along the ‘pledge-and-review’ cycle of the Paris Agreement.

To address this issue, in this essay we propose a framework that can serve as a blueprint for enhancing the effectiveness and accountability of NDCs, while providing transparency into the ‘pledge-and-review’ cycle of the Paris Agreement by leveraging the contributions that open science can bring to the process. The framework targets practitioners, such as modellers and expert analysts, and outlines clear steps to help them building capacity in adopting and developing open-source tools, open data, and relevant open science practices. In this way, the framework is intended to provide practitioners with the basis to tap into the wide range of open science resources available to support their analysis, while also deliver enhanced transparency of their own analytical efforts. Additionally, the framework outlines how these skills and expertise equip practitioners with a unique set of competences well-suited to enable them to play a key role in providing technical support along the monitor, track, assess, report, and review process that aims at ensuring the realisation of NDC pledges.

With this framework, we emphasise for the first time the relevance and applicability of open-source tools and open data, along with related practices, in providing systematic and coherent analytical methods and resources to support systems planning and analysis, which are crucial for informing the NDC formulation. Simultaneously, we underscore the contribution that the consistent implementation of open science practices can make to enhancing transparency and accountability in the ‘pledge-and-review’ process of the Paris Agreement, through distinctive features like the release of data and models under open licenses to facilitate their accessibility and scrutiny by stakeholders, and the use of metadata to document analytical processes and methods underlying modelling and planning efforts. Furthermore, by relying on open science resources and practices as its backbone, we highlight how the framework offers a solution to lower the need for financial resources to support capacity-building efforts by taking advantage of the lower entry barriers that open science offers, by removing the need for proprietary software licenses, and by providing a plethora of free training material and knowledge resources for practitioners to tap into. This is particularly relevant for supporting developing countries’ efforts in building and maintaining the technical competencies they need to take full ownership of their country’s NDCs and to participate in the ‘pledge-and-review' cycle. Finally, with this framework we identify clear steps in the implementation cycle of the Paris Agreement that provide practitioners with a comprehensive overview of their role and contribution to the global climate governance. In this way, we aim to empower practitioners and encourage them to develop greater reliance on science-based evidence throughout the process, thus becoming the technical experts needed to support more effective and accountable planning and decision-making.

The framework is designed to address several of the gaps and needs currently affecting the full implementation and realisation of the Paris Agreement, by increasing transparency in the process and by providing tools and resources to deliver effective and accountable NDCs. The framework is also designed to leverage the cascading positive effect of established principles and practices for open-source tools and open data, to deliver enhanced credibility and legitimacy of the climate governance process. Relying on established open science practices offers practitioners an entry point to a more collaborative and accessible approach to systems planning and analysis. Ultimately, this aims to enable a sustainable learning cycle and lay the foundation for societal discussion on the commitment to climate change mitigation action.

We do not expect that transitioning toward the implementation of the proposed framework is going to be straightforward. It requires careful consideration of how different national cultural, social, and organisational contexts may influence the level of acceptance and uptake of an open and transparent approach to systems planning and analysis [74]. In consideration of this, we think it is worth highlighting that open science practices provide flexibility, allowing varying degrees of openness and transparency in their implementation, unlocking corresponding levels of scrutiny and trust creation. Therefore, future research could further explore potential barriers to the adoption of open science, particularly in selected developing countries, taking into consideration context-specific preferences and needs with regards to the competencies, tools, and resources currently lacking. In addition, future research could focus on defining incremental degrees of openness for NDC development, corresponding to a set of implementable practices towards full openness and transparency. Finally, the available literature has repeatedly highlighted the positive influence of open science practices on promoting a more participatory science-policy interface, moving towards an increasingly dynamic model of engagement between practitioners and decision-makers [75,76]. Nonetheless, there remains a lack of empirical evidence supporting this positive effect of open science. Consequently, future research should focus on exploring how open science has been received by stakeholders and society as part of existing processes that made use of open models and data to support decision-making. This could begin with interviews with stakeholders involved in the development of the Costa Rica NDC presented in this paper.

Ultimately, with this essay we bring to the forefront the relevance and applicability of open science to NDC development. We do so by proposing a framework to embed transparency and accountability along the ‘pledge-and-review’ cycle of the Paris Agreement, while addressing particularly the developing countries’ needs for resources and expertise for NDC preparation. Our goal is to support practitioners in embracing their role and finding new, more accessible ways to develop skills and expertise, and actively contribute to shaping climate action.

Supporting information

S1 Text. Supplementary material to this essay, containing the methodology and key definitions of Open Science related to the essay.

https://doi.org/10.1371/journal.pclm.0000839.s001

(PDF)

S1 Fig. Conceptual diagram linking concepts across the Articles of the Paris Agreement to the “pledge-and-review” process for implementation.

https://doi.org/10.1371/journal.pclm.0000839.s002

(PDF)

Acknowledgments

The authors of this paper would like to thank Dr. Francesco Fuso Nerini for his valuable inputs in the finalisation of the manuscript.

References

  1. 1. Held D, Roger C. Three models of global climate governance: from Kyoto to Paris and beyond. Glob Policy. 2018;9(4):527–37.
  2. 2. Falkner R. The Paris Agreement and the new logic of international climate politics. Int Aff. 2016;92(5):1107–25.
  3. 3. Keohane RO, Oppenheimer M. Paris: beyond the climate dead end through pledge and review? Polit Gov. 2016;4(3):142–51.
  4. 4. UNFCCC. Reference Manual for the Enhanced Transparency Framework under the Paris Agreement [Internet]. UNFCCC; 2022 June [cited 2022 Dec 19]. Available from: https://unfccc.int/documents/268136
    • 5. Leiter T. Nationally determined contributions (NDCs) as a governance instrument – accounting for politics, negotiation progress, and related mechanisms under the Paris Agreement. Environ Polit. 2023;0(0):1–6.
    • 6. UNFCCC. Glasgow Climate Pact [Internet]. Conference of the Parties serving as the meeting of the Parties to the Paris Agreement. Report No.: 1/CMA.3. 2021 Oct [cited 2024 Mar 11]. Available from: https://unfccc.int/decisions?f%5B0%5D=body%3A1343&f%5B1%5D=body%3A4099&f%5B2%5D=symboldec%3A1/CMA.3
      • 7. UNFCCC. Paris Agreement [Internet]. United Nations Framework Convention on Climate Change (UNFCCC). 2015 [cited 2021 Nov 14]. Available from: https://unfccc.int/process/conferences/pastconferences/paris-climate-change-conference-november-2015/paris-agreement
      • 8. UNFCCC. UNFCCC. Nationally Determined Contributions (NDCs). 2022 [cited 2023 Jan 6]. Available from: https://unfccc.int/process-and-meetings/the-paris-agreement/nationally-determined-contributions-ndcs
      • 9. Khan M, Mfitumukiza D, Huq S. Capacity building for implementation of nationally determined contributions under the Paris Agreement. Clim Policy. 2020;20(4):499–510.
      • 10. Pauw WP, Castro P, Pickering J, Bhasin S. Conditional nationally determined contributions in the Paris Agreement: foothold for equity or Achilles heel?. Clim Policy. 2020;20(4):468–84.
      • 11. Dagnet Y, Van Asselt H, Cavalheiro G, Theoto Rocha M, Bisiaux A, Cogswell N. Designing the Enhanced Transparency Framework, Part 2: Review Under the Paris Agreement [Internet]. Wahington, DC: Project for Advancing Climate Action Transparency (PACT). 2017 Nov p. 44. Available from: https://www.wri.org/research/designing-enhanced-transparency-framework-part-2-review-under-paris-agreement
      • 12. Röser F, Widerberg O, Höhne N, Day T. Ambition in the making: analysing the preparation and implementation process of the Nationally Determined Contributions under the Paris Agreement. Clim Policy. 2020;20(4):415–29.
      • 13. Pauw WP, Klein RJT. Beyond ambition: increasing the transparency, coherence and implementability of Nationally Determined Contributions. Clim Policy. 2020;20(4):405–14.
      • 14. Pauw WP, Klein RJT, Mbeva K, Dzebo A, Cassanmagnago D, Rudloff A. Beyond headline mitigation numbers: we need more transparent and comparable NDCs to achieve the Paris Agreement on climate change. Clim Change. 2017;147(1–2):23–9.
      • 15. UNESCO. UNESCO Recommendation on Open Science [Internet. Report No.: SC-PCB-SPP/2021/OS/UROS. Paris: UNESCO; 2021 [cited 2022 Oct 8]. Available from: https://unesdoc.unesco.org/ark:/48223/pf0000379949?posInSet=3&queryId=cdb6384d-230d-4b28-8ccc-ccf9a3c5fb3d
        • 16. Pfenninger S, DeCarolis J, Hirth L, Quoilin S, Staffell I. The importance of open data and software: Is energy research lagging behind?. Energy Policy. 2017;101.
        • 17. DeCarolis J, Daly H, Dodds P, Keppo I, Li F, McDowall W, et al. Formalizing best practice for energy system optimization modelling. Appl Energy. 2017;194:184–98.
        • 18. Süsser D, Ceglarz A, Gaschnig H, Stavrakas V, Flamos A, Giannakidis G, et al. Model-based policymaking or policy-based modelling? How energy models and energy policy interact. Energy Res Soc Sci. 2021;75:101984.
        • 19. Huntington HG, Weyant JP, Sweeney JL. Modeling for insights, not numbers: the experiences of the energy modeling forum. Omega. 1982;10(5):449–62.
        • 20. Sweeney JL, Weyant JP. The Energy Modeling Forum: An Overview [Internet]. Report No.: EMF PP 6.2. Stanford (CA): Stanford University; 1979 Mar [cited 2024 Feb 29]. Available from: https://web.stanford.edu/group/emf-research/new-emf.stanford.edu/publications/emf_pp_62__the_energy_modeling_forum_an_overview/index.html
          • 21. Ellenbeck S, Lilliestam J. How modelers construct energy costs: Discursive elements in Energy System and Integrated Assessment Models. Energy Res Soc Sci. 2019;47:69–77.
          • 22. Pfenninger S, Hawkes A, Keirstead J. Energy systems modeling for twenty-first century energy challenges. Renew Sustain Energy Rev. 2014;33:74–86.
          • 23. Jones RN, Patwardhan A, Cohen SJ, Dessai S, Lammel A, Lempert RJ. Foundations for decision making. In: Field CB, Barros VR, editors. Climate change 2014: impacts, adaptation, and vulnerability: Working Group II contribution to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, UK and New York (NY): Cambridge University Press; 2014. p. 195–228.
            • 24. Moser S. Making a difference on the ground: the challenge of demonstrating the effectiveness of decision support. Clim Change. 2009;95(1–2):11–21.
            • 25. Weyant J. Some Contributions of Integrated Assessment Models of Global Climate Change. Rev Environ Econ Policy. 2017;11(1):115–37.
            • 26. Pollitt H, Mercure J-F, Barker T, Salas P, Scrieciu S. The role of the IPCC in assessing actionable evidence for climate policymaking. NPJ Clim Action. 2024;3(1).
            • 27. Henke H, Dekker M, Lombardi F, Pietzcker R, Fragkos P, Zakeri B, et al. Comparing energy system optimization models and integrated assessment models: Relevance for energy policy advice. Open Res Eur. 2024;3:69. pmid:38665265
            • 28. Süsser D, Gaschnig H, Ceglarz A, Stavrakas V, Flamos A, Lilliestam J. Better suited or just more complex? On the fit between user needs and modeller-driven improvements of energy system models. Energy. 2022;239:121909.
            • 29. Klosterman RE. Simple and Complex Models. Environ Plann B Plann Des. 2012;39(1):1–6.
            • 30. Head BW. Policy analysis: Evidence based policy-making. International Encyclopedia of the Social & Behavioral Sciences. Elsevier; 2015. p. 281–7.
              • 31. Weikmans R, Asselt H van, Roberts JT. Transparency requirements under the Paris Agreement and their (un)likely impact on strengthening the ambition of nationally determined contributions (NDCs). Clim Policy. 2019;20(4):511–26.
              • 32. Ciplet D, Adams KM, Weikmans R, Roberts JT. The transformative capability of transparency in global environmental governance. Glob Environ Polit. 2018;18(3):130–50.
              • 33. Niet T, Shivakumar A, Gardumi F, Usher W, Williams E, Howells M. Developing a community of practice around an open source energy modelling tool. Energy Strat Rev. 2021;35:100650.
              • 34. Gardumi F, Shivakumar A, Morrison R, Taliotis C, Broad O, Beltramo A, et al. From the development of an open-source energy modelling tool to its application and the creation of communities of practice: The example of OSeMOSYS. Energy Strat Rev. 2018;20:209–28.
              • 35. Bazilian M, Rice A, Rotich J, Howells M, DeCarolis J, Macmillan S, et al. Open source software and crowdsourcing for energy analysis. Energy Policy. 2012;49:149–53.
              • 36. Campbell-Duruflé C. Clouds or Sunshine in Katowice? Transparency in the Paris Agreement Rulebook. Carbon Clim Law Rev. 2018;12(3):209–17.
              • 37. Gupta A, van Asselt H. Transparency in multilateral climate politics: Furthering (or distracting from) accountability? Regulat Gov. 2017;13(1):18–34.
              • 38. Suiseeya MK, Elhard DK, Paul CJ. Toward a relational approach in global climate governance: Exploring the role of trust. WIREs Clim Change. 2021;12(4).
              • 39. Konrad S, van Deursen M, Gupta A. Capacity building for climate transparency: neutral ‘means of implementation’ or generating political effects? Clim Policy. 2021;:1–19.
              • 40. Pfenninger S. Open code and data are not enough: understandability as design goal for energy system models. Prog Energy. 2024;6(3):033002.
              • 41. DeCarolis JF, Jaramillo P, Johnson JX, McCollum DL, Trutnevyte E, Daniels DC, et al. Leveraging Open-Source Tools for Collaborative Macro-energy System Modeling Efforts. Joule. 2020;4(12):2523–6.
              • 42. Wilkinson MD, Dumontier M, Aalbersberg IJJ, Appleton G, Axton M, Baak A, et al. The FAIR Guiding Principles for scientific data management and stewardship. Sci Data. 2016;3:160018. pmid:26978244
              • 43. Bulkeley H. Climate policy and governance: an editorial essay. WIREs Clim Change. 2010;1(3):311–3.
              • 44. Winkler H, Mantlana B, Letete T. Transparency of action and support in the Paris Agreement. Clim Policy. 2017;17(7):853–72.
              • 45. OLCreate. OpenLearn Create. Climate Compatible Growth April 2023 courses. 2023 [cited 2025 Nov 5]. Available from: https://www.open.edu/openlearncreate/course/index.php?categoryid=896
              • 46. CCG. Climate Compatible Growth. OpenLearn Courses. 2025 [cited 2025 Nov 5]. Available from: https://climatecompatiblegrowth.com/international-partnerships/capacity-building/openlearn-courses/
              • 47. CCG. Climate Compatible Growth. Energy Modelling Community. 2025 [cited 2025 Nov 5]. Available from: https://climatecompatiblegrowth.com/international-partnerships/capacity-building/energy-modelling-community/
              • 48. European Commission. CORDIS - EU research results. RE-thinking of approaches and toolkits for transdisciplinary INTEGRATEd assessment of climate-compatible energy strategies from the African Union through to the European Union. 2025 [cited 2025 Nov 9]. Available from: https://cordis.europa.eu/project/id/101118217/results
              • 49. RE-INTEGRATE. re-integrate-au.eu. Rethinking integrated energy strategies: Bridging continents, shaping the future, uniting for renewable energy. 2025 [cited 2025 Nov 29]. Available from: https://www.re-integrate-au.eu/
              • 50. RE-INTEGRATE. Re-integrate-au.eu. About - RE-INTEGRATE in a nutshell. 2025 [cited 2025 Nov 9]. Available from: https://www.re-integrate-au.eu/?page_id=52
              • 51. European Commission. EU Funding & Tenders Portal. AU-EU Energy System Modelling. 2022 [cited 2025 Nov 9]. Available from: https://ec.europa.eu/info/funding-tenders/opportunities/portal/screen/opportunities/topic-details/HORIZON-CL5-2022-D3-02-02
              • 52. Chacon S, Straub B. Pro Git [Internet]. Version 2.1.429. Apress; 2024 [cited 2024 May 27]. Available from: https://git-scm.com/book/en/v2
                • 53. GitHub, Inc. GitHub. GitHub.com; 2024 [cited 2024 May 27]. Available from: https://github.com
                  • 54. UNFCCC. Further guidance in relation to the mitigation section of decision 1/CP.21 [Internet]. Conference of the Parties serving as the meeting of the Parties to the Paris Agreement. Report No.: 4/CMA.1. 2018 Dec [cited 2024 Mar 1]. Available from: https://unfccc.int/decisions?f%5B0%5D=body%3A4099&f%5B1%5D=symboldec%3A4/CMA.1
                  • 55. UNFCCC. Modalities, procedures and guidelines for the transparency framework for action and support referred to in Article 13 of the Paris Agreement [Internet]. Conference of the Parties serving as the meeting of the Parties to the Paris Agreement; 2018 Dec [cited 2024 Mar 1]. Report No.: 18/CMA.1. Available from: https://unfccc.int/decisions?f%5B0%5D=body%3A4099&f%5B1%5D=symboldec%3A18/CMA.1
                    • 56. UNFCCC. Guidance for operationalizing the modalities, procedures and guidelines for the enhanced transparency framework referred to in Article 13 of the Paris Agreement [Internet]. Conference of the Parties serving as the meeting of the Parties to the Paris Agreement. Report No.: 5/CMA.3. 2021 Oct [cited 2024 Mar 1]. Available from: https://unfccc.int/decisions?f%5B0%5D=body%3A4099&f%5B1%5D=symboldec%3A5/CMA.3
                    • 57. Costa Rica Gobierno del Bicentenario, Ministerio de Ambiente y Energía (MINAE), Direccion de Cambio Climatico (DCC). Contribución Nacionalmente Determinada de Costa Rica 2020 [Internet]. UNFCCC; 2020 Dec [cited 2022 Nov 30]. Available from: https://unfccc.int/NDCREG
                      • 58. Government of Costa Rica. National Decarbonization Plan [Internet]. 2018 [cited 2024 Apr 4]. Available from: https://unfccc.int/sites/default/files/resource/NationalDecarbonizationPlan.pdf
                      • 59. ICTP. The Abdus Salam International Centre for Theoretical Physics. The Summer School on Modelling Tools for Sustainable Development - OpTIMUS | (smr 3210) (04-29 June 2018). [cited 2024 Mar 20]. Available from: https://indico.ictp.it/event/8315/
                      • 60. Howells M, Rogner H, Strachan N, Heaps C, Huntington H, Kypreos S, et al. OSeMOSYS: The Open Source Energy Modeling System, An Introduction to Its Ethos, Structure and Development. Energy Policy. 2011;39(10):5850–70.
                      • 61. EPERLab. EPERLab/OSeMOSYS-CR [Internet]. 2019 [cited 2024 Mar 11]. Available from: https://github.com/EPERLab/OSeMOSYS-CR
                      • 62. Godínez-Zamora G, Victor-Gallardo L, Angulo-Paniagua J, Ramos E, Howells M, Usher W, et al. Decarbonising the transport and energy sectors: Technical feasibility and socioeconomic impacts in Costa Rica. Energy Strat Rev. 2020;32:100573.
                      • 63. World Bank. Decarbonization Pathways Modeling in Costa Rica [Internet]. (Partnership for Market Readiness - Costa Rica Program). Washington D.C.: World Bank; 2020 [cited 2024 Mar 11]. Available from: https://documents1.worldbank.org/curated/en/923041617603388931/pdf/Partnership-for-Market-Readiness-Costa-Rica-Program-Decarbonization-Pathways-Modeling-in-Costa-Rica.pdf
                        • 64. Howells M, Quiros-Tortos J, Morrison R, Rogner H, Niet T, Petrarulo L, et al. Energy system analytics and good governance -U4RIA goals of Energy Modelling for Policy Support [Internet]. In Review; 2021 Mar [cited 2024 Mar 8]. Available from: https://www.researchsquare.com/article/rs-311311/v1
                        • 65. Ramos EP, Howells M, Sridharan V, Engström RE, Taliotis C, Mentis D, et al. The Climate, Land, Energy, and Water systems (CLEWs) framework: a retrospective of activities and advances to 2019. Environ Res Lett. 2020.
                        • 66. Howells M, Hermann S, Welsch M, Bazilian M, Segerström R, Alfstad T, et al. Integrated analysis of climate change, land-use, energy and water strategies. Nature Clim Change. 2013;3(7):621–6.
                        • 67. Bazilian M, Rogner H, Howells M, Hermann S, Arent D, Gielen D, et al. Considering the energy, water and food nexus: Towards an integrated modelling approach. Energy Policy. 2011;39(12):7896–906.
                        • 68. Rodríguez-Arce M, Angulo-Paniagua J, Victor-Gallardo L, Roccard J, Quirós-Tortós J, Alexander K, et al. Integrated climate, land, energy, and water framework to support the Nationally Determined Contribution updating process [Internet]. 2024 [cited 2024 Mar 11]. Available from: https://www.sciencedirect.com/science/article/pii/S2211467X24000233
                        • 69. EPERLab-UCR. OSeMOSYS-CR. The OSeMOSYS – CR model — OSeMOSYS-CR 1.0.a documentation. 2020 [cited 2024 Mar 11]. Available from: https://osemosys-cr.readthedocs.io/en/latest/index.html#
                        • 70. World Bank. PARTNERSHIP FOR MARKET READINESS – COSTA RICA PROGRAM: Program Alignment and Achievements, Overview and Key Lessons [Internet]. (Partnership for Market Readiness - Costa Rica Program). Washington D.C.SA: World Bank; 2020. Available from: https://openknowledge.worldbank.org/server/api/core/bitstreams/d00ab589-6cef-5809-b935-600afed3b6ef/content
                          • 71. Sistema Nacional de Métrica del Cambio Climático (SINAMECC). SINAMECC. Sobre SINAMECC. 2021 [cited 2024 Mar 11]. Available from: http://www.sinamecc.go.cr/acerca-de
                          • 72. Jaramillo M, Quirs-Torts J, Vogt-Schilb A, Money A, Howells M. Data-to-Deal (D2D): Open Data and Modelling of Long Term Strategies to Financial Resource Mobilization - the case of Costa Rica [Internet]. Cambridge Open Engage; 2023 [cited 2024 Mar 5]. Available from: https://www.cambridge.org/engage/coe/article-details/640997370e6a36fabafbcae3
                          • 73. Groves DG, Syme J, Molina-Perez E, Calvo Hernandez C, Víctor-Gallardo LF, Godinez-Zamora G, et al. The Benefits and Costs of Decarbonizing Costa Rica’s Economy: Informing the Implementation of Costa Rica’s National Decarbonization Plan Under Uncertainty [Internet]. RAND Corporation; 2020 Nov [cited 2024 Mar 8]. Available from: https://www.rand.org/pubs/research_reports/RRA633-1.html
                          • 74. Beltramo A, Leonard A, Tomei J, Usher W. Data governance and open science in energy planning: A case study of the Kenyan ecosystem. Energy Res Soc Sci. 2024;118:103821.
                          • 75. Reichmann S, Wieser B. Open science at the science-policy interface: bringing in the evidence? Health Res Policy Syst. 2022;20(1):70. pmid:35725491
                          • 76. Sokolovska N, Fecher B, Wagner GG. Communication on the Science-Policy Interface: An Overview of Conceptual Models. Publications. 2019;7(4):64.