Summary Sustainability Science PDF

\*Underline the concepts in the CCS question\* First explain concepts shortly Don't forget mini discussion (contradiction) Inhoudsopgave {#inhoudsopgave.Kopvaninhoudsopgave} ============= [Alphabetical order 4](#alphabetical-order) [Tutorial 2 6](#tutorial-2) [Key Concepts 6](#key-concepts) [Study and Discussion Questions 9](#study-and-discussion-questions) [Tutorial 3 12](#tutorial-3) [Key Concepts 12](#key-concepts-1) [Tutorial 4 14](#tutorial-4) [Key concepts 14](#key-concepts-2) [Tutorial 5 17](#tutorial-5) [Key concepts 17](#key-concepts-3) [Tutorial 6 23](#tutorial-6) [Key concepts 23](#key-concepts-4) [Tutorial 7 25](#tutorial-7) [Key concepts 25](#key-concepts-5) [Figures and tables 29](#figures-and-tables) [CCS Case information 31](#ccs-case-information) [CCS Case questions 33](#ccs-case-questions) [Task 2: Potential benefits and pitfalls 33](#task-2-potential-benefits-and-pitfalls) [Task 3: Knowledge types and worldviews on CCS 35](#task-3-knowledge-types-and-worldviews-on-ccs) [Task 4: CCS problems and Hoppe's typology 39](#task-4-ccs-problems-and-hoppes-typology) [Task 5: Boundary work in the realm of CCS 42](#task-5-boundary-work-in-the-realm-of-ccs) [Task 6: Roles of scientist 43](#task-6-roles-of-scientist) Alphabetical order ================== 1. **Action Research** - Week 5, Original Number: 14 2. **Adaptive Enterprise** - Week 7, Original Number: 5 3. **Autonomy and Authority of Science** - Week 5, Original Number: 6 4. **Boundary Object** - Week 5, Original Number: 10 5. **Boundary Organization** - Week 5, Original Number: 9 6. **Boundary Work** - Week 5, Original Number: 8 & Week 7, Original Number: 8 7. **Brokerage** - Week 6, Original Number: 7 8. **Caretaking** - Week 3, Original Number: 11 9. **Citizen Science** - Week 4, Original Number: 6 10. **Complex Adaptive Systems** - Week 2, Original Number: 2 11. **Complex Systems Approach** - Week 7, Original Number: 14 12. **Complexity** - Week 2, Original Number: 10 13. **Critical Transformational Approach** - Week 7, Original Number: 15 14. **Critical-Transformational Transdisciplinarity** - Week 7, Original Number: 20 15. **Criteria for Usable Knowledge** - Week 5, Original Number: 12 16. **Collaborative Enterprise** - Week 7, Original Number: 4 17. **Descriptive-Analytical Orientation** - Week 7, Original Number: 12 18. **Disciplinary** - Week 5, Original Number: 16 19. **Dynamic Knowledge Integration** - Week 4, Original Number: 4 20. **Epistemic Role of Stakeholder Involvement** - Week 7, Original Number: 17 21. **Evidence Synthesis** - Week 6, Original Number: 8 22. **Extended Peer Community** - Week 2, Original Number: 20 & Week 7, Original Number: 19 23. **Feedback Interactions/Loops** - Week 2, Original Number: 8 24. **Framing** - Week 4, Original Number: 13 25. **Geographical Scale** - Week 2, Original Number: 7 26. **Human-Nature Connectedness** - Week 3, Original Number: 5 27. **Hypothesis and Falsification** - Week 5, Original Number: 4 28. **Indigenous and Local Knowledge (ILK)** - Week 4, Original Number: 5 29. **Indigenous Worldview** - Week 3, Original Number: 4 30. **Interconnectedness** - Week 3, Original Number: 8 31. **Interdependence** - Week 3, Original Number: 9 32. **Joint Knowledge Production** - Week 4, Original Number: 16 33. **Knowledge Integration** - Week 4, Original Number: 4 34. **Knowledge Pluralization** - Week 3, Original Number: 12 35. **Knowledge Types** - Week 3, Original Number: 6 36. **Legitimacy** - Week 7, Original Number: 3 37. **Linear Model** - Week 5, Original Number: 7 38. **Linear Model of Science-Policy Relation** - Week 5, Original Number: 19 39. **Linear Model of Science-Policy-Society Interaction** - Week 5, Original Number: 24 40. **Moderately Structured Problem (Ends)** - Week 4, Original Number: 12 41. **Moderately Structured Problem (Means)** - Week 4, Original Number: 11 42. **Multiple Evidence Based Approach (MEB)** - Week 4, Original Number: 7 43. **Participatory Knowledge Production** - Week 5, Original Number: 13 44. **Politicization of Science** - Week 5, Original Number: 21 45. **Pragmatist Perspective** - Week 7, Original Number: 11 46. **Quality of Knowledge** - Week 5, Original Number: 20 47. **Reciprocity** - Week 3, Original Number: 10 48. **Reflexive Knowledge Production** - Week 3, Original Number: 7 49. **Reflexivity** - Week 7, Original Number: 10 50. **Saliency** - Week 7, Original Number: 1 51. **Science and Non-science** - Week 5, Original Number: 3 52. **Scientific Knowledge** - Week 5, Original Number: 5 53. **Scientization of Politics** - Week 5, Original Number: 22 54. **Structured Problem** - Week 4, Original Number: 9 55. **Sustainability Science** - Week 2, Original Number: 16 56. **Target Knowledge** - Week 4, Original Number: 2 57. **The 5 Capital Assets** - Week 2, Original Number: 4 58. **The Honest Broker** - Week 6, Original Number: 4 59. **The Honest Issue Advocate** - Week 6, Original Number: 5 60. **The Issue Advocate** - Week 6, Original Number: 3 61. **The Pure Scientist** - Week 6, Original Number: 1 62. **The Science Arbiter** - Week 6, Original Number: 2 63. **Time Scale** - Week 2, Original Number: 6 64. **Tipping Points/Regime Shifts** - Week 2, Original Number: 11 65. **Transformative Knowledge** - Week 4, Original Number: 3 66. **Transformative Orientation** - Week 7, Original Number: 13 67. **Transition Management** - Week 7, Original Number: 18 68. **Values and Facts** - Week 2, Original Number: 14 69. **Vulnerability and Resilience** - Week 2, Original Number: 12 70. **Wicked Problem** - Week 2, Original Number: 13 71. **Worldview** - Week 3, Original Number: 1 72. **Worldview Construct** - Week 3, Original Number: 2 Tutorial 2 ========== Key Concepts ------------ **1. Social-Environmental System (Matson, 2016)** A **social-environmental system** is a network of human and environmental components that continuously interact and evolve. These systems are influenced by human actions (like policy or technology) and environmental processes (such as climate change). Understanding them is essential for predicting the outcomes of interventions that aim to improve social well-being while protecting the environment. **2. Complex Adaptive Systems (Matson, 2016)** These are systems where components interact in dynamic, non-linear ways and can adapt to changing conditions. Their behavior is unpredictable due to feedback loops, tipping points, and external influences. For example, ecosystems and economies both exhibit these characteristics. **CCS case** Non-linear and feedback loop can cause things. For example, more CO₂ is captured, industrial activities may increase, leading to unintended consequences like energy demand growth or shifts in public policy. **3. Stocks and Flows (Matson, 2016)** **Stocks** represent the assets or resources (natural, human, social, manufactured, knowledge) in a system, while **flows** are the movements of those resources between parts of the system. Managing these is key to maintaining balance within social-environmental systems. **4. The 5 Capital Assets (Matson, 2016)** Sustainability requires the management of five key types of capital: - **Natural Capital**: Ecosystems and natural resources. - **Manufactured Capital**: Infrastructure and goods. - **Human Capital**: Skills, knowledge, health. - **Social Capital**: Networks, trust, institutions. - **Knowledge Capital**: Information and intellectual resources. **CCS case** - **Natural Capital**: Geological formations capable of storing CO₂. - **Manufactured Capital**: Infrastructure like CCS plants, pipelines, and storage facilities. - **Human Capital**: The skills and knowledge of engineers and policymakers involved in CCS. - **Social Capital**: Public trust and institutional frameworks that support CCS implementation. - **Knowledge Capital**: Scientific and technical information about CCS technologies and their environmental impacts. **5. Systems Boundary (Kates, 2001)** The **boundary** of a system defines what is inside and outside the system being studied. It\'s important to clarify this when analyzing interactions. Systems can be defined on different **geographical** (local to global) and **time scales** (past, present, future). **6. Time Scale (Kates, 2001)** Looking at both short-term and long-term impacts is essential when analyzing social-environmental systems. Some effects of actions might only become visible far into the future, such as climate change or ecosystem degradation. **7. Geographical Scale (Kates, 2001)** Systems operate at multiple levels, from local to global. Decisions made at one level can have ripple effects at others. For example, local agricultural practices can have global environmental consequences (e.g., pollution reaching international waters). **8. Feedback Interactions/Loops (Kates, 2001)** A **feedback loop** occurs when a change in one part of the system influences another part, which then affects the original part. These loops can either reinforce changes (positive feedback) or mitigate them (negative feedback). Example: economic growth leading to environmental degradation, which then reduces economic growth in the long term. **9. Invisibilities in Space and Time (Kates, 2001)** Decisions often have unseen consequences, categorized into: - **Spatial Invisibility**: Impacts in distant areas are overlooked (e.g., pollution moving across borders). - **Social Invisibility**: Effects on marginalized communities are ignored. - **Temporal Invisibility**: Long-term consequences for future generations are underestimated. **10. Complexity (Kates, 2001)** Complex systems have many interacting components, leading to unpredictable outcomes. The **interconnectedness** of these systems means small changes can lead to significant, unexpected consequences. **11. Tipping Points/Regime Shifts (Kates, 2001)** A **tipping point** is a critical threshold where a system undergoes a significant change, potentially shifting to a new state (regime shift). These changes can be abrupt and irreversible, such as species extinction or large-scale climate change. **12. Vulnerability and Resilience (Kates, 2001)** - **Vulnerability** refers to a system\'s susceptibility to harm from external shocks (e.g., natural disasters, economic crises). - **Resilience** is the system's ability to recover from disturbances and return to its original state or adapt to new conditions. **13. Wicked Problem (Hoppe, 2018; Kates, 2001))** A **wicked problem** is one that is highly complex, with no clear solution due to competing values, incomplete information, and high uncertainty (e.g., climate change, biodiversity loss). **14. Values and Facts (Funtowicz, 1993)** In complex systems, decision-making is not just about scientific facts but also involves **values**---the ethical, social, and political dimensions that influence which outcomes are prioritized. Sustainability decisions often reflect this balance of facts and values. **15. Dealing with Uncertainties (Funtowicz, 1993)** Uncertainty is inherent in managing complex systems. New approaches to decision-making under uncertainty, such as **adaptive management** and **precautionary approaches**, are essential to navigating unknowns in sustainability challenges. **16. Sustainability Science** An interdisciplinary field focused on the interactions between human and ecological systems. It seeks to address sustainability challenges through: - **Problem-driven research** - **Cross-scale integration** (global and local) **CCS case** Linking local CCS projects to global climate goals. - **Participatory approaches** involving stakeholders and the public. **17. Mode 1/Normal Science (Funtowicz, 1993)** **Normal science** operates within established frameworks to solve specific problems. It focuses on minimizing uncertainties, working within accepted paradigms, and producing incremental scientific advancements. **18. Mode 2/Post-Normal Science (Funtowicz, 1993)** **Post-normal science** addresses high-uncertainty, high-stakes problems that normal science cannot handle alone. It involves wider stakeholder participation, emphasizes the role of values and ethics, and deals with complex problems where facts are often uncertain, and decisions have significant consequences (e.g., climate policy). This is important for CCS. **19. Linear Model of Science-Policy Relation (Funtowicz, 1993)** The **linear model** suggests that scientific knowledge leads directly to policy decisions. This model is outdated because it overlooks the complexity of how knowledge, societal values, and politics interact in decision-making. **20. Extended Peer Community (Funtowicz, 1993)** In **post-normal science**, an extended peer community includes not only traditional scientists but also stakeholders, activists, and the public. This inclusive approach ensures that diverse knowledge and perspectives contribute to solving complex sustainability problems. - Pros: More resilient, increased engagement/involvement, inclusive knowledge, enhanced quality. - Cons: Slow decision-making, lack of knowledge, lack of common understanding. **21. Issue-Driven Research (Funtowicz, 1993)** **Issue-driven research** focuses directly on addressing real-world problems. It is often interdisciplinary, involving collaborations across various fields to tackle challenges like climate change or resource management. **Matson\'s Perspective on Social-Environmental Systems** **Key Insight**:\ Matson emphasizes the importance of understanding the full dynamics of **social-environmental systems** to avoid negative consequences of human interventions. Historically, there has been a focus on human and economic systems at the expense of long-term environmental health. For example: - Increased food production has led to pesticide and fertilizer runoff, harming ecosystems. - In the past, poor public health interventions (e.g., cholera in London) exacerbated problems due to a lack of understanding of environmental systems. **Funtowicz: Post-Normal Science and Policy** **Post-normal science** deals with policy issues where: - **Uncertainty is high** (e.g., environmental changes). - **Decision stakes are significant**, with long-term, broad impacts. It emphasizes the importance of addressing both facts and values and calls for broader participation through **extended peer communities**. **Kates: Sustainability Science** **Sustainability science** seeks to integrate knowledge across scales and disciplines to address issues like climate change and sustainable development. It involves: - **Participatory approaches** that include scientists, policymakers, and citizens. - A focus on both **simultaneous exploration and application** of findings. - Addressing **uncertainty** and fostering **collaborative solutions**. Study and Discussion Questions ------------------------------ **Dynamic Social-Environmental Systems (Matson et al.)** Dynamic social-environmental systems are interconnected networks of human and environmental elements that evolve due to interactions between human actions (e.g., policies, technologies) and natural processes. These systems are influenced by both internal and external pressures, leading to continuous adaptation. **Five Key Challenges of Complex Social-Environmental Systems (Matson et al.)** 1. **Nonlinearity**: Systems often react unpredictably. 2. **Feedback Loops**: Components influence each other, sometimes amplifying effects. 3. **Cross-Scale Interactions**: Local decisions can have global impacts. 4. **Invisibility**: Some impacts are hidden across time and space. 5. **Tipping Points**: Systems can experience sudden, dramatic shifts. **Normal (Mode 1) Science (Funtowicz and Ravetz)** Mode 1 science involves solving specific, well-defined problems within established frameworks. It assumes stability and minimizes uncertainty, making progress gradually through incremental steps. **Post-Normal Science (Funtowicz and Ravetz)** Post-normal science is needed for complex, high-stakes issues where traditional methods fall short. With high uncertainty and major consequences, decisions must balance scientific evidence with ethical and social considerations. This approach is crucial for addressing sustainability challenges like climate change. **Sustainability Science (Kates et al.)** Sustainability science focuses on understanding and managing interactions between social and ecological systems to meet human needs while preserving the environment. It emphasizes: 1. **Complex Systems**: Addressing multiple, sometimes irreversible stresses. 2. **Interdisciplinary Research**: Combining varied methods and perspectives. 3. **Participatory Approaches**: Engaging stakeholders to ensure relevance. 4. **Integration Across Scales**: Linking global processes with local contexts. **Why Is Sustainability Difficult? (Matson et al.)** Sustainability is challenging due to: - Complexity and interconnectedness of systems. - Invisible impacts. - Conflicting interests among stakeholders. - Uncertainty and long-term risks. - Sudden tipping points. **Post-Normal Science and Sustainability (Funtowicz and Ravetz)** Post-normal science is critical for sustainability due to high uncertainty, complexity, and conflicting values in decision-making. **Extended Peer Community (Funtowicz and Ravetz)** An extended peer community involves a broad range of participants, including local stakeholders and the public, in the decision-making process. This enriches knowledge, improves relevance, and leads to more credible and collaborative solutions. However, it may also introduce challenges, such as lack of expertise or decision paralysis. **Simultaneous Scientific Exploration and Application (Kates et al.)** Simultaneously applying and exploring scientific research, especially in areas like climate change, can increase the relevance and speed of solutions. While it ensures timely, real-world impact, it risks compromising scientific rigor due to pressure or bias. A balanced approach that maintains scientific integrity while addressing practical concerns is essential. Tutorial 3 ========== Key Concepts ------------ **1. Worldview (Van Opstal & Hugé, 2013, p. 688)** A worldview is the lens through which individuals or groups perceive and understand the world. It combines value orientations and cultural perspectives (Van Egmond & De Vries, 2011). **2. Worldview construct (Van Opstal & Hugé, 2013, p. 691)** The process of collectively developing shared interpretations and understandings, allowing diverse worldviews to co-evolve and produce common goals (Van Opstal, 2013). **3. Modern worldview (Van Opstal & Hugé, 2013, p. 695)** Based on linear time, anthropocentrism (human-centered), scientific rationalism, and a focus on materialism and land ownership. This worldview often marginalizes nature and Indigenous perspectives (Van Opstal, 2013). **4. Indigenous worldview (Van Opstal & Hugé, 2013, p. 695)** Characterized by a holistic perspective where time is nonlinear, humans are part of nature, and everything is interconnected spiritually and physically. The land is seen as sacred, and nature is viewed as alive. **5. Human-nature connectedness (Fitzpatrick, 2023, p. 19)** The deep relationship between humans and the natural world, focusing on understanding how humans are an integral part of ecosystems. Indigenous knowledge systems emphasize this connection (Fitzpatrick, 2023). **6. Knowledge types (Fitzpatrick, 2023, p. 20,21)** - **Indigenous Knowledge**: Grounded in holistic, place-based worldviews that emphasize the interconnectedness of all beings, often underrepresented in mainstream sustainability discussions. **E.g. Maori** - **Local/Place-Based Knowledge**: Knowledge rooted in specific locations, blending nature and culture, often overlapping with Indigenous practices and offering valuable insights into sustainable living. **E.g. small farming in Mexico** - **Systems Thinking**: A scientific approach that looks at complex, interconnected systems, like ecosystems and societies, and how they regulate and respond to change, essential for managing sustainability transformations**. E.g. Gaia** - **Spiritual/Religious Knowledge**: Involves spiritual or religious values that guide sustainable practices, seen in both Indigenous and non-Western religions like Buddhism, and Christian stewardship**. E.g. Hawaiians or Buddhist ecology**. - **Subjective/Inner Knowledge**: Personal, reflective insights that foster self-awareness and pro-environmental behavior, often through practices like mindfulness. - **Relational Thinking**: Focuses on interconnected relationships between humans and non-human entities, giving agency to nature and challenging human-centered views, often linked to Indigenous perspectives. **7. Reflexive knowledge production (Fitzpatrick, 2023, p. 36)** The self-awareness and critical reflection on one\'s knowledge, ensuring that biases are acknowledged, and diverse perspectives are incorporated. **Cycle**: Continuous questioning of what defines our worldviews -\> holistic reframing of our worldviews to adopt new context. **8. Interconnectedness (Fitzpatrick, 2023, p. 82)** The concept that all elements of the world (human, non-human, spiritual) are interrelated, emphasizing the mutual dependencies that shape existence. **9. Interdependence (Mazzocchi, 2020, p. 82)** The reliance between humans and nature, where actions in one area have repercussions in others. This is a key feature of Indigenous knowledge systems **10. Reciprocity (Mazzocchi, 2020, p. 82)** A principle in Indigenous worldviews, emphasizing the need to give back to nature and maintain balance through mutual exchanges, often seen as a sacred duty. **11. Caretaking (Mazzocchi, 2020** The responsibility to take care of the land and the natural environment as stewards, a key aspect of Indigenous knowledge systems that contrasts with exploitative practices (Mazzocchi, 2020). Closely connected with reciprocity, interdependence, interconnectedness. **12. Knowledge pluralization (Van Opstal & Hugé, 2013, p. 697)** The recognition and inclusion of multiple forms of knowledge (scientific, Indigenous, local) to address sustainability challenges, promoting epistemic diversity. **13. Local place-based knowledge (Fitzpatrick, 2023, p. 21)** **Local/Place-Based Knowledge**: Knowledge rooted in specific locations, blending nature and culture, often overlapping with Indigenous practices and offering valuable insights into sustainable living. **E.g. small farming in Mexico.** Knowledge tied to specific geographical and cultural contexts, focusing on the unique ways communities interact with their local environments. Often overlaps with Indigenous knowledge (Fitzpatrick, 2023). Tutorial 4 ========== Key concepts ------------ **1. Systems knowledge (Karrasch et al., 2022, p. 15)** Refers to understanding the components, interactions, and context of a problem within a system. It emphasizes describing the current state and relationships among elements within socio-ecological systems. Integration of local knowledge and scientific data helps reduce informational uncertainties and build a shared understanding. **2. Target knowledge (Karrasch et al., 2022, p. 15, 16)** Focuses on how systems *ought* to be, reflecting the goals, values, and motives of different actors. It involves setting goals based on a collective vision for a desired future state. Integration aims to align different stakeholders\' motives. **3. Transformative knowledge (Karrasch et al., 2022, p. 16)** Involves knowledge about how to achieve change and implement solutions. It includes practical strategies, tools, and empowerment processes to facilitate transformations in socio-ecological systems. **4. Knowledge integration (Karrasch et al., 2022)** A continuous learning process that brings together various forms of knowledge (scientific, practical, and experiential) without simply summing them up. It's crucial for addressing sustainability challenges by enabling co-produced, solutions-oriented research. **5. Indigenous and local knowledge (ILK) (Tengö et al., 2021, p. 504)** ILK refers to knowledge that has been developed through long-term interactions with the environment, often passed down through generations. It includes insights into social, cultural, and ecological practices that are critical for sustainable resource management. **Example**: Indigenous communities in the Amazon have extensive knowledge of the medicinal properties of local plants, which is crucial for biodiversity conservation. **6. Citizen science (Tengö et al., 2021)** Citizen science involves the participation of non-professionals in scientific research. It democratizes data collection and analysis by allowing citizens to contribute to scientific knowledge and be part of decision-making processes. **Example**: Volunteers monitoring water quality in local rivers to contribute to environmental conservation efforts. **7. Multiple Evidence Based approach (MEB) (Tengö et al., 2021, p. 508, fig 2)** The MEB approach is a framework that integrates different knowledge systems (scientific, indigenous, local) without diminishing the integrity of each. It seeks to weave together multiple forms of evidence to build a more holistic understanding of complex problems. **Example**: Integrating traditional Indigenous knowledge with scientific studies to manage fish populations in coastal areas. **8. Problem structuring (Hoppe, 2018)** **Definition**: Problem-structuring is a process of transforming vague and unmanageable issues into clear, well-defined problems. It involves breaking down complex challenges into smaller, more actionable components. This is crucial in policy design and decision-making. **Example**: Decomposing a large-scale environmental issue like climate change into manageable sub-problems such as renewable energy adoption or emissions reduction in specific sectors. **9. Structured problem (Hoppe, 2018)** A structured problem is one where both the goals and means to achieve them are clearly defined. These problems are relatively easy to solve because there is a clear understanding of both what needs to be done and how to do it. **Example**: Implementing recycling programs in a city where the infrastructure and public support are well-established. **10. Unstructured problem (Hoppe, 2018)** An unstructured problem is one where neither the goals nor the means are clearly defined, making it difficult to address. These are often referred to as \"wicked problems\" because of their complexity and uncertainty. **Example**: Global poverty, where there is no consensus on the root causes or the best way to solve it. **11. Moderately structured problem (means) (Hoppe, 2018)** A moderately structured problem (means) occurs when the goals are clear, but the means to achieve them are uncertain. The challenge lies in identifying the right strategies or tools to achieve known objectives. **Example**: Reducing carbon emissions is a clear goal, but finding the most effective combination of technologies and policies to achieve it is uncertain. **12. Moderately structured problem (ends) (Hoppe, 2018)** A moderately structured problem (ends) is when the means to solve a problem are clear, but the goals are not well-defined. Stakeholders may disagree on what the end result should be. **Example**: In education policy, there may be consensus on the best teaching methods, but disagreement about whether the goal should be preparing students for jobs or fostering critical thinking. **13. Framing (Hoppe, 2018)** Framing refers to the way problems are perceived, defined, and presented. It highlights certain aspects of an issue while downplaying others, often influencing policy responses and public perception. **Example**: Presenting climate change as a global security threat rather than an environmental issue can lead to different policy approaches. **14. Science-based framing (Hoppe, 2018)** Science-based framing emphasizes framing issues primarily through scientific data and evidence, which can guide policy decisions. It provides a factual and objective basis for understanding complex problems. *Focuses only on scientific methods, often seeing them as superior. **(views participants as data providers**)* **Example**: Framing deforestation as a measurable cause of carbon emissions and biodiversity loss using satellite data and ecological studies. **15. Knowledge system approach (Tengö et al., 2021)** The knowledge system approach recognizes that knowledge is produced, shared, and used within specific social, cultural, and political contexts. It calls for integrating diverse knowledge systems (e.g., scientific, indigenous, local) to address complex problems more effectively. *Values all knowledge systems (scientific, Indigenous, local) as equally important and complementary. (**views participants as knowledge holders**).* **Example**: Combining local farmer knowledge with academic agricultural research to improve crop yields in a specific region. **16. Joint knowledge production (Karrasch, 2022)** Collaborative processes where different stakeholders (scientists, policymakers, local communities, etc.) co-produce knowledge that is relevant and usable for solving complex problems. It emphasizes the value of co-creation, involving multiple perspectives and expertise. **Example**: Scientists and Indigenous communities working together to design wildlife conservation strategies that are culturally appropriate and scientifically effective. Tutorial 5 ========== Key concepts ------------ **1. Objectivity and truth (Turnhout & Haffman, 2012)** *(Table 2.1)* Objectivity refers to knowledge that is believed to be unbiased and independent of individual perspectives or preferences. **In science**, objectivity is sought to achieve truth, which is seen as an accurate representation of reality. **2. Subjectivity and power (Turnhout & Haffman, 2012)** *(Table 2.1)* Subjectivity acknowledges that knowledge is influenced by individual perspectives, experiences, and contexts. Power dynamics, such as who controls knowledge production, play a significant role in shaping what is considered valid knowledge. **Decision-making** processes are often related withs subjectivity and power. **3. Science and non-science (Turnhout & Haffman, 2012, p. 27)** The distinction between science and non-science is not a fixed or inherent difference but rather something socially constructed. The process of demarcation involves drawing boundaries between what is recognized as legitimate scientific knowledge and what is considered non-scientific. This boundary is shaped through social, political, and academic discussions, meaning it can shift depending on the context. Science is typically seen as knowledge that follows systematic methods, empirical testing, and is viewed as objective and rigorous. Non-science, on the other hand, includes areas of knowledge such as belief systems or pseudosciences. **4. Hypothesis and falsification (Turnhout & Haffman, 2012, p. 21)** Karl Popper introduced **falsification** as a way to distinguish science from non-science. He argued that scientific theories should be structured so they can be tested and potentially proven false. Unlike previous approaches based on induction (building theories from observations), Popper emphasized that [no amount of positive evidence can fully confirm a theory, but one contradictory (negative) observation can disprove it]. However, in practice, this is complicated by the use of instruments and interpretations that involve assumptions. Even a simple hypothesis, like \"all swans are white,\" can be challenged based on definitions and observational limits. **5. Scientific knowledge (Turnhout & Haffman, 2012)** *(Table 2.1)* Scientific knowledge refers to empirically validated understanding, derived from experiments and observations, that contributes to the body of work used to explain natural phenomena. **6. Autonomy and authority of science (Turnhout & Haffman, 2012)** Refers to the idea that it operates independently from political or social pressures, while its authority reflects society\'s trust in its methods and findings. However, this is complicated by boundary work and external influences. **Example**: Lysenkoism in the Soviet Union under Stalin. Trofim Lysenko, a biologist, rejected true genetic science for political reasons, and his false ideas were backed by the government. Scientists who disagreed were punished, and this led to crop failures and famine. It shows how science can fail when politics takes over (Haffman, 2012, p. 23). **7. Linear model (Turnhout & Haffman, 2012, p. 25)** The **linear model** of science suggests that basic scientific research naturally leads to technological advancements and societal benefits, with minimal outside intervention. It envisions a one-way flow from research to practical application, where science is separate from decision-making and politics. However, this model has several **limitations**: 1. Science can produce harm, not just benefits, as seen with environmental issues or nuclear weapons. 2. Basic science is often influenced by political and industrial interests. 3. Knowledge doesn't automatically get used in policy; it requires interaction and communication. 4. The model assumes that better understanding of science leads to better decisions, ignoring the complexities of real-world decision-making. **8. Boundary work (Turnhout & Haffman, 2012; Wieglieb & Bruns, 2023)** *(Fig. 1 Clark et al., 2011, p. 4616) (Fig. 1 Wieglieb & Bruns, 2023, p. 1072)* Refers to the effort to draw and maintain distinctions between science and non-science, deciding what counts as credible scientific knowledge. These boundaries aren\'t fixed or universal but are shaped by people, institutions, tools, and language, depending on context. For example, courts decide what scientific evidence is admissible, and schools debate whether creationism qualifies as science. **Limitations** 1. **Subjectivity**: Boundaries are not based on objective criteria but are socially constructed, meaning they can be influenced by personal, political, or institutional interests. 2. **Inconsistency**: Boundaries can change over time and across contexts, making it difficult to establish clear, lasting rules for what counts as science. 3. **Power Dynamics**: Those with influence (like policymakers or powerful institutions) can shape boundaries to exclude certain types of knowledge, which may limit diversity in scientific thought or practice. 4. **Conflict and Exclusion**: Drawing boundaries often results in excluding certain types of expertise, which can prevent valuable interdisciplinary cooperation or dismiss alternative viewpoints. **9. Boundary object (Turnhout & Haffman, 2012; Clark et al., 2011)** A boundary object is something that facilitates collaboration across different domains (e.g., science and policy) while allowing each group to interpret it in its own way. "They play an important role in the facilitation of cooperation across differences (between science and non-science, or between different scientists" (Turnhout & Haffman, 2012, p. 33). They play an important part in facilitation of corporation (Turnhout & Haffman, 2012, p. 33). **Example**, a climate change model might be used by scientists to study environmental patterns and by policymakers to make policy decisions. - Example in linear model: (IPCC) reports: scientific findings-\> policy makers - Limitations: one-way flow of info, insufficient engagement, not flexible. - Example in co-production model: participatory modelling tool (e.g. interactive climate map of flood risks) - Limitations: power imbalances (science/policy\>public), conflicting expectations, complexity. **10. Boundary organization (Wieglieb & Bruns, 2023)** Boundary organizations act as intermediaries between science and policy, facilitating dialogue and collaboration across the two domains. They help ensure that scientific knowledge is usable for policy decisions. They operate in the hybrid (co-productionist) space between science and policy (*Fig. 1 Wieglieb & Bruns, 2023, p. 1072)*. Example: IPCC, connects policy-makers with scientist. - Limitations: slow decisions, political influence, local context, and translation form science to policy. **11. Demarcation and coordination (Wieglieb & Bruns, 2023)** Demarcation refers to drawing clear boundaries between different fields (e.g., science and politics). Coordination is the process of aligning these fields to facilitate effective collaboration and application of scientific knowledge. **Example**: - **Demarcation** here involves judges deciding whether a particular piece of evidence, such as DNA analysis, is scientifically valid and can be admitted in court. - **Coordination** occurs when these scientific reports are translated into actionable goals and agreements, such as the **Paris Agreement**, where nations commit to specific emissions reduction targets based on scientific recommendations. **12. Criteria for usable knowledge (Turnhout & Haffman, 2012, p. 35)** (Weiss) Usable knowledge is scientific information that is not only credible but also relevant, timely, and accessible to policymakers and other stakeholders. **Criteria for Usable Knowledge**: 1. **Credibility**: Knowledge must be scientifically valid and reliable, ensuring trust from decision-makers. 2. **Relevance (Salience)**: It should address the specific needs of users, ensuring its applicability to real-world problems. 3. **Legitimacy**: The process should be fair and inclusive, ensuring trust from diverse stakeholders. 4. **Timeliness**: Knowledge must be available when decisions need to be made. 5. **Understandability**: It should be communicated clearly, so non-experts can grasp and act on it. 6. **Actionability**: It should provide practical steps for decision-makers to implement.. **13. Participatory knowledge production (Turnhout & Haffman, 2012)** This involves active collaboration between scientists and non-scientific actors, such as the public or policymakers, to ensure that the knowledge produced is relevant and applicable to real-world problems. - Forms: Citizen science and co-production **Limitations:** - **Data quality concerns**: Non-experts may collect inconsistent or inaccurate data. - **Limited influence**: Participants typically contribute to data collection but not the broader research process or decisions. - **Time and resource-intensive**: Requires significant time to engage all stakeholders and reach consensus. - **Power imbalances**: Experts may still dominate the process, limiting true collaboration. - **Local focus**: Findings may not be generalizable beyond the specific community or issue. - **Complex coordination**: Managing active involvement at every stage can be challenging and resource-heavy. **14. Action research (Turnhout & Haffman, 2012)** A collaborative research approach where researchers work directly with communities or stakeholders to address specific, often local, problems, with the aim of both producing knowledge and driving social change. **Participatory Action Research (PAR)**: Researchers work directly with communities or groups to address local issues, with participants actively involved in every step of the research. - **Similarity**: Combines scientific research with community needs. - **Difference**: PAR is deeply focused on solving specific local problems and empowering communities. **15. Transdisciplinary research (Clark et al., 2011)** Goes beyond academia to include non-scientific stakeholders (e.g., policymakers, communities) in the research process. ASB embraced a transdisciplinary approach by ensuring local and international researchers worked together on context-specific and generalizable knowledge, involving stakeholders in decision-making and agenda-setting to ensure the research addressed real-world problems. **16. Disciplinary (Clark et al., 2011)** Focuses on a single discipline with a defined set of methods and rules. For example, natural scientists in ASB initially tried to address complex social issues independently but produced limited results, showing that disciplinary research alone wasn\'t sufficient for such complex problems. **17. Multidisciplinary (Clark et al., 2011)** Involves multiple disciplines working parallel on the same problem but without integrating their approaches. ASB initially employed multidisciplinary strategies where natural and social scientists worked separately but contributed to a larger goal. While some shared data collection protocols were established, each discipline largely kept to its own methods. **18. Interdisciplinary (Clark et al., 2011)** Different disciplines work together, integrating methods and knowledge to address a problem. In ASB, true interdisciplinary work emerged later with bioeconomic models that combined natural and social science approaches, leading to deeper collaboration and more credible research outcomes. **19. Transdisciplinary (Clark et al., 2011)** Knowledge from multiple disciplines and non-academic stakeholders is integrated, focusing on complex societal challenges that require collaboration beyond academic disciplines. **20. Quality of knowledge (Wieglieb & Bruns, 2023)** The quality of knowledge refers to its credibility, relevance, usability, and legitimacy, particularly in how it is applied to real-world problems. **21. Politicization of science** Politicization occurs when scientific research or its findings are influenced by political agendas, often leading to biased or selective use of scientific knowledge. **Example**: COVID-19 Vaccine**s**: During the COVID-19 pandemic, the approval and distribution of vaccines became highly politicized. In some regions, political figures promoted certain treatments or dismissed scientific guidance for political gain, leading to public mistrust of vaccines. - **Impact**: This undermined public health efforts and slowed vaccination rates in some areas. **22. Scientization of politics** This refers to situations where political decisions are increasingly justified by scientific evidence, often marginalizing other forms of knowledge, such as ethical or social considerations*.* **Example**: **Nuclear Energy Debates**: In decisions around nuclear energy, policymakers often lean heavily on scientific data about safety and efficiency while disregarding ethical concerns, such as long-term waste management or the social acceptability of nuclear power. - **Impact**: This scientization can lead to technocratic decision-making that overlooks public opinion and other non-scientific considerations. **23. Co-productionist perspective (Wieglieb & Bruns, 2023)** The co-productionist perspective emphasizes the mutual shaping of science, policy, and society. It recognizes that knowledge production and decision-making are interconnected processes influenced by various stakeholders. (Fig. 1, Wieglieb & Bruns, 2023, p. 1072) **24. Linear model of science-policy-society interaction (Turnhout & Haffman, 2012, p. 25)** The **linear model** of science-policy-society interaction suggests that scientific knowledge flows directly from research to societal benefits, assuming science is autonomous and self-regulating. It envisions that scientific discoveries automatically lead to practical applications and sound decisions. **Problems with the Linear Model:** 1. **Science can cause harm** (e.g., nuclear weapons, environmental issues). 2. **Not independent**: Research is influenced by political and industrial interests. 3. **Knowledge isn\'t automatically used**: Policymakers often don\'t apply scientific findings as intended. 4. **Oversimplified decision-making**: It ignores the complexity of politics, ethics, and societal factors in policy decisions. Tutorial 6 ========== Key concepts ------------ **1. The pure scientist (Pielke, 2007)** *(Fig. 2.1, Pielke, 2007, p. 19)* Focuses solely on research and avoids engaging with how knowledge is applied in policy decisions.\ **Example**: A scientist measuring carbon emissions without suggesting solutions.\ **Limitation**: The pure scientist\'s detachment from policy means their research may not directly influence decision-making, limiting the practical impact of their work. **2. The science arbiter (Pielke, 2007)** *(Fig. 2.1, Pielke, 2007, p. 19)* Answers specific questions from decision-makers without pushing for a particular outcome.\ **Example**: Calculating how much CO2 emissions would decrease by switching to electric buses without advocating for or against the policy.\ **Limitation**: The role is reactive, responding only to posed questions, which can limit the arbiter\'s ability to influence broader or more nuanced policy issues. **3. The issue advocate (Pielke, 2007)** *(Fig. 2.1, Pielke, 2007, p. 19)* Promotes a particular policy or course of action based on their scientific findings, narrowing choices to steer decisions toward a preferred outcome.\ **Example**: Advocating for renewable energy as the best climate change solution.\ **Limitation**: Advocates risk being perceived as biased, which can undermine the credibility of their scientific objectivity in polarized debates. **4. The honest broker (Pielke, 2007)** *(Fig. 2.1, Pielke, 2007, p. 19)* Expands the decision-making space by presenting various policy options along with their associated risks and benefits.\ **Example**: Offering policymakers a range of energy solutions, from solar to nuclear, outlining the advantages and trade-offs of each.\ **Limitation**: Decision-makers may become overwhelmed with too many options or view the broker as indecisive, which can hinder actionable outcomes. **5. The honest issue advocate (Bohman, 2018)** Combines scientific integrity with advocacy, promoting specific actions while being transparent about preferences and the scientific basis behind them.\ **Limitation**: Despite transparency, honest issue advocates may still face accusations of bias or "stealth advocacy" from stakeholders who disagree with their preferred actions. **6. Science advisory ecosystem (Gluckman, Bardsley & Kaiser, 2021)** A **science advisory ecosystem** refers to the network of mechanisms, institutions, and individuals that provide scientific advice to governments, policymakers, and the public. These ecosystems are composed of various elements like commissions, committees, academies, and individual science advisors. Their primary role is to offer expert input, guidance, and evidence to inform decisions in areas such as health, environment, and technology. **Limitations:** - **Complexity and Fragmentation**: The fragmented nature of science advisory systems, with multiple sources of advice, can lead to conflicting recommendations and make it harder for decision-makers to act. - **Influence of Non-Scientific Factors**: Even the best scientific advice may be overridden by political, economic, or social considerations. This challenges the assumption that science alone can guide policy. - **Values and Bias**: Although scientific advisors aim for neutrality, they cannot entirely remove values from the advice they give. This value-laden nature of knowledge production can sometimes lead to accusations of bias or advocacy. **7. Brokerage (Gluckman, Bardsley & Kaiser, 2021)** Knowledge brokers bridge the gap between science and policy by translating and synthesizing scientific evidence to inform decision-making. **Example**: The role of the **National Institute for Health and Care Excellence (NICE)** in the UK. NICE synthesizes medical research and clinical data to provide evidence-based recommendations on healthcare treatments and policies for the National Health Service (NHS). For example, NICE reviews scientific evidence on the cost-effectiveness of new drugs and treatments and advises the NHS on whether to adopt them. **Limitation**: Knowledge brokers may struggle with maintaining neutrality, and they often face challenges in communicating complex scientific uncertainties to policymakers who expect clear, actionable insights. **8. Evidence synthesis (Gluckman, Bardsley & Kaiser, 2021)** The process of combining findings from multiple studies to provide a comprehensive understanding of an issue, often through systematic reviews or meta-analyses.\ **Limitation**: Evidence synthesis can be time-consuming and may struggle to keep pace with fast-moving policy needs, making it less useful in urgent decision-making contexts. **9. Knowledge brokerage (Gluckman, Bardsley & Kaiser, 2021)** Involves not only synthesizing evidence but also actively facilitating the communication between scientists and policymakers, ensuring research is applicable and useful in decision-making. **Example**: The role played by the **Intergovernmental Panel on Climate Change (IPCC)**. The IPCC synthesizes climate science research and facilitates communication between scientists and policymakers by producing reports that inform global climate policy, such as the Paris Agreement. Their reports translate complex scientific data on climate change into accessible information for governments to use in policy decisions. **Limitation**: The IPCC can unintentionally blur the lines between neutral evidence synthesis and subtle advocacy, particularly when dealing with politically charged topics like carbon emissions reduction, where scientific consensus may conflict with economic or political interests. This can lead to accusations of bias, even when the intent is to remain objective. Tutorial 7 ========== Key concepts ------------ Saliency\ - Credibility\ - Legitimacy\ - Collaborative enterprise\ - Systems enterprise\ - Adaptative enterprise\ - Political enterprise\ - Boundary work\ - Transdisciplinarity\ - Reflexivity\ - Pragmatist perspective\ - Descriptive-analytical orientation\ - Transformative orientation\ - Complex systems approach\ - Transformational approach\ - Social role of stakeholder involvement\ - Epistemic role of stakeholder involvement\ - Transition management\ - Extended peer community\ - Critical-transformational transdisciplinarity **1. Saliency (Matson et al., 2016)** **Is it relevant?** Do potential users of the provided knowledge see it as relevant? **Example**: A project addressing agricultural irrigation issues in a drought-prone region would have saliency if the knowledge produced helps local farmers manage water use more effectively. **Limitation**: If researchers misinterpret the local context, such as introducing an irrigation technology that is not suitable for the region's soil or social dynamics, the knowledge may be irrelevant, reducing its saliency. Researchers could also create problems that were not there before, misinterpreting what is wrong in a case. **2. Credibility (Matson et al., 2016)** **Is it true?** Do potential users of the provided knowledge actually believe that the person or organization providing the knowledge know what they are talking about? **Example**: A climate model showing accurate temperature predictions based on peer-reviewed methodologies would be seen as credible. **Limitation**: If a study uses outdated data or flawed methods, stakeholders may question the accuracy, and the knowledge will lose credibility. **3. Legitimacy (Matson et al., 2016)** **Is it fair/unbiased?** Refers to the fairness and ethical integrity of the knowledge production process. Legitimate knowledge is produced impartially, without conflicts of interest or hidden agendas, and incorporates diverse perspectives fairly. **Example**: A CCS initiative that involves local communities in decision-making and considers their concerns about potential risks (such as leakage) will enhance legitimacy. **Limitation**: If a researcher is funded by an oil company and their study favors fossil fuel use, stakeholders may view it as biased, undermining the legitimacy of the research. **4. Collaborative enterprise (Matson et al., 2016)** Involves different stakeholders---scientists, policymakers, and communities---working together to co-create knowledge that addresses complex societal problems. Collaboration helps integrate diverse perspectives for holistic solutions. This links knowledge to action. **Limitation**: If one group dominates the decision-making process, collaboration may become superficial, limiting the mutual benefits and learning. **5. Adaptive enterprise (Matson et al., 2016)** Focuses on the need for continuous learning and adaptation in response to evolving challenges. This approach recognizes that solutions must remain flexible and responsive to feedback and changing conditions. Adaptive enterprises facilitate learning from encounters with a spatially varied and temporally changing world. **Example**: A climate adaptation plan that evolves based on new data about shifting weather patterns, allowing for continuous adjustments. **Limitation**: Overly rigid or \"one-size-fits-all\" approaches struggle to address unique or changing local conditions, making them ineffective over time. **6. Systems enterprise (Matson et al., 2016)** An approach that sees problems as part of broader interconnected systems. Understanding the interactions and relationships within systems is key to solving complex issues, recognizing that changes in one area can have ripple effects elsewhere. **Limitation**: Fragmented knowledge and poor communication between disciplines can lead to disjointed or incomplete solutions that fail to address the system as a whole. **7. Political enterprise (Matson et al., 2016)** Refers to the involvement of political actors and institutions in the process of knowledge production. It underscores how political influences, power dynamics, and governance structures shape research processes and outcomes. Many researchers resist the notation that science and politics are intertwined. However, when knowledge becomes salient for society/political decision makers, then it becomes power. And then both knowledge users and producers become part of a political enterprise. **Limitation**: Scientists may resist engaging in politics, fearing it could compromise their perceived objectivity, thus limiting their influence on policy. **8. Boundary work (Matson et al., 2016)** Boundary work signifies the processes through which the research community interacts with the world of action and policy-making. The idea of boundary work is that tension between actors with different views must be managed effectively. **Example**: Scientists conducting CCS research collaborate with regulators, industries, and environmental NGOs to bridge the gap between academic research and practical policy implementation, creating a successful boundary-spanning initiative. **Limitation**: If tensions between environmental NGOs (concerned about long-term safety) and industrial stakeholders (focused on cost) are not properly managed, the CCS project may stall due to conflicting priorities **9. Transdisciplinary (Bergman et al., 2023)** A research approach that integrates knowledge from multiple disciplines and engages stakeholders outside academia (e.g., community members, policymakers) to co-produce solutions for complex real-world issues like sustainability. **10. Reflexivity (Popa et al., 2015)** Involves critical self-reflection by researchers and stakeholders on their assumptions, values, and power dynamics. Reflexivity encourages a deeper understanding of how different knowledge systems and perspectives influence research outcomes. GROUP PROCES. Important to have open dialogue, it can allow for someone to be perceived as more trustworthy. **Key aspects:** - Collaborative deliberation on values and assumptions - Social experimentation and learning - Extending the peer community - Addressing power structures in knowledge production **11. Pragmatist perspective (Popa et al., 2015)** This perspective focuses on practical solutions to real-world problems. It emphasizes problem-solving and the utility of knowledge, prioritizing outcomes that work in practice, regardless of the theory behind them. In this approach participants are led to question and jointly reframe their values and understandings. Happens often through joint experimentation and social learning. **12. Descriptive-analytical orientation (Popa et al., 2015)** A research approach that aims to describe and analyze existing conditions or systems without necessarily aiming to change them. It focuses on understanding how things are, rather than how they should be. Orientation based on advanced modelling tools. **13. Transformative orientation (Popa et al., 2015)** Aims to drive systemic, fundamental change in systems, rather than merely describing or improving existing processes. It seeks to shift paradigms and create new, sustainable pathways for action. Orientation based on collaborative problem-solving processes with the view to directly contribute to the transition process towards more sustainable societies. **14. Complex systems approach (Popa et al., 2015)** Recognizes that real-world problems are interconnected and involve many interacting components. This approach emphasizes interconnectedness, non-linearity, emergence of new properties, adaptive capacity, and a holistic perspective. **Limitation**: Tends to maintain a distinction between scientific reliability and social legitimacy, often lacks sufficient consideration of non-scientific expertise in research design **15. Critical transformational approach (Popa et al., 2015)** Focused on achieving deep, systemic changes, especially for sustainability issues. It emphasizes inclusivity, stakeholder participation, critical reflection on power dynamics, and long-term sustainability. Aims to actively contribute to sustainability transitions through research; Emphasizes the need for societal change and innovation; Integrates knowledge production with practical action and social learning; More explicitly incorporates reflexivity on values and normative orientations; Challenges traditional boundaries between science and society. **16. Social role of stakeholder involvement (Popa et al., 2015)** This role emphasizes the dimensions of democratic participation, social relevance and legitimacy building. It calls for rethinking of the values, norms and responsibilities guiding scientific research. It makes sure scientific knowledge is socially robust and perceived by society to be both transparent and participative. **17. Epistemic role of stakeholder involvement (Popa et al., 2015)** Involves stakeholders in the actual creation of knowledge, recognizing that their experiential or indigenous knowledge is critical to understanding complex systems. Their insights contribute to the co-production of scientifically and socially relevant knowledge. **18. Transition management (Popa et al., 2015)** A transformative dimension to the complex systems approach, it recognizes the interconnection of understanding and use of knowledge and therefore it emphasizes the need to combine theoretical construction with a solution-oriented approach for implementing various transition pathways **19. Extended peer community (Popa et al., 2015)** Refers to broadening the range of stakeholders who are involved in the research process, especially beyond traditional scientific experts, to include diverse voices such as community members, practitioners, and local knowledge holders. **20. Critical-transformational transdisciplinarity (Popa et al., 2015)** A research approach that not only integrates knowledge across disciplines and stakeholders but also critically reflects on the power dynamics and values involved. It aims for deep social transformation and sustainability through co-production of knowledge. Figures and tables ================== **Tengö et al Fig 2** Afbeelding met tekst, illustratie Automatisch gegenereerde beschrijving **Wieglieb & Bruns figure** ![Afbeelding met tekst, schermopname, cirkel, diagram Automatisch gegenereerde beschrijving](media/image2.png) **Clark et al., figure** Afbeelding met tekst, Lettertype, schermopname, nummer Automatisch gegenereerde beschrijving **Pielke figure** ![Afbeelding met tekst, Rechthoek, schermopname, diagram Automatisch gegenereerde beschrijving](media/image4.png) CCS Case information ==================== The **Porthos project** (Port of Rotterdam CO₂ Transport Hub and Offshore Storage) is one of the largest **Carbon Capture and Storage (CCS)** projects in Europe, aimed at significantly reducing CO₂ emissions from industrial activity in the Port of Rotterdam, the Netherlands. The project captures carbon dioxide emitted by industries, transports it via pipelines, and stores it in depleted gas fields beneath the North Sea. **Key Information About the Porthos Project:** 1. **Purpose and Goal**: - The primary goal of Porthos is to help the Netherlands meet its climate targets by reducing industrial CO₂ emissions. - Porthos aims to capture around **2.5 million tonnes of CO₂ per year** from industries in the Rotterdam area, representing a significant portion of the region\'s industrial emissions. - The captured CO₂ will be transported through a pipeline to offshore gas fields in the North Sea, which are no longer in use, where it will be permanently stored. 2. **How it Works**: - **Capture**: Industries in the Port of Rotterdam capture CO₂ before it is released into the atmosphere. - **Transport**: The CO₂ is compressed and transported through an onshore pipeline to a compressor station. It is then transported offshore through a pipeline under the North Sea. - **Storage**: The CO₂ is injected into depleted gas fields, stored at a depth of about 3 kilometers beneath the seabed. 3. **Significance**: - It is a cornerstone of the Dutch government\'s climate strategy, particularly its **2030 Climate Agreement**, which aims to reduce CO₂ emissions by 49% (compared to 1990 levels). - Porthos plays a role in the **European Green Deal**, as the EU seeks to reduce greenhouse gas emissions to net zero by 2050. - This project sets an example of how **CCS technology** can contribute to decarbonizing hard-to-abate industries like oil refining, chemicals, and steel. **Key Stakeholders:** 1. **Port of Rotterdam Authority**: - The Port Authority is one of the leading partners in the project. They are crucial in providing infrastructure for the CO₂ pipeline and facilitating the collaboration between industries and governmental bodies. 2. **Energie Beheer Nederland (EBN)**: - EBN is a state-owned company that is responsible for managing the Netherlands' natural gas resources. It is a key partner in the Porthos project, focusing on the safe storage of CO₂ in depleted gas fields. 3. **Gasunie**: - Gasunie is the state-owned operator of the natural gas network in the Netherlands. For Porthos, it oversees the construction and operation of the pipelines and infrastructure required for CO₂ transportation from land to the offshore storage site. 4. **Industrial Emitters**: - Companies in the Port of Rotterdam area, including large refineries and chemical plants, are the primary **emitters** participating in the project. These companies capture their CO₂ emissions and send them into the Porthos system. Major participants include **Air Liquide**, **Air Products**, **ExxonMobil**, and **Shell**. 5. **Dutch Government**: - The Dutch Ministry of Economic Affairs and Climate Policy is a key government stakeholder, providing regulatory support and financial incentives for CCS development as part of the Netherlands' broader climate strategy. 6. **European Union**: - The EU provides financial and regulatory support through initiatives like the **Innovation Fund** and other green transition frameworks under the **European Green Deal**. 7. **Environmental Groups and NGOs**: - Environmental organizations and non-governmental organizations (NGOs) are also key stakeholders, both as supporters and critics. Some groups support Porthos as a necessary step to decarbonize industry, while others criticize it for perpetuating reliance on fossil fuel-based industries rather than focusing solely on renewable energy. 8. **Local Communities and Businesses**: - Communities and businesses in the Rotterdam region have a stake in the project's potential economic impacts, including job creation and local investment, as well as concerns over environmental risks associated with CO₂ transportation and storage. **Challenges and Considerations:** - **Public Acceptance**: CCS projects often face opposition due to concerns about the safety of CO₂ storage and fears of potential leakage, which must be addressed through public engagement. - **Financing and Costs**: The project requires significant investment, but CCS technology is still relatively costly. The success of Porthos depends on continued governmental subsidies and carbon pricing policies. - **Regulatory Compliance**: Ensuring that the CO₂ storage complies with environmental regulations, such as those governing offshore activity, is essential for long-term project success. CCS Case questions ================== Task 2: Potential benefits and pitfalls --------------------------------------- **Question 1A: Explain the potential benefits and pitfalls of using Carbon Capture and Underground Storage (CCS/CCUS) to reach net zero targets.** **Potential Benefits ** *1. Mitigating industrial emissions* CCS/CCUS technology enables critical high-emission sectors, such as the steel, aviation, agriculture and cement industries, to offset their emissions from ongoing operations. These sectors provide inputs to critical sectors, such as construction and transportation, and thus limiting their operation may negatively undermine economic growth, particularly in developing nations. Additionally, CCS technology provides the option to achieve negative emissions in other sectors such as agriculture through the introduction of Bioenergy with CCS (Bruckner et al. 2014). *2. Leveraging Existing Infrastructure * CCS can be retrofitted onto existing industrial facilities and power plants, extending the life of infrastructure while reducing emissions. This is particularly valuable for regions heavily dependent on fossil fuels. Moreover, in instances where fossil fuel-powered infrastructure has not yet reached the end of its lifespan, the introduction of CCS technology will prevent early decommissioning and associated losses (IEA, 2020). *3. Opportunity for job creation and ongoing economic growth* CCS technology will provide an opportunity for greater job creation in fields such as engineering and project management, as these facilities will require both construction and ongoing operation and maintenance. The introduction of this technology will enable fossil fuel-dependent regions to maintain employment and energy generation in the medium term while a long-term just transition to renewable energy is achieved. Moreover, in countries whose energy mix is substantially composed of fossil fuels, CCS technology also enables them to continue meeting baseline supply as renewable energy is introduced over the medium- and long-term (Townsend, 2020). *4. Other pollutants can be removed simultaneously.* Oxyfuel combustion employs much oxygen, significantly reducing nitrogen oxide (NOx) and sulphur dioxide emissions. One study conducted for the Argonne National Laboratory discovered that oxyfuel combustion produced 50% less NOx gas than normal air combustion (Rhode, 2021). **Potential Pitfalls ** *1. High Costs and Economic Barriers* Building CCS plants is still very expensive to achieve. As we need to transport the CO2 Emissions often from places quite far away for the Pothos Project, Germany\'s Ruhr Gebiet will also offset emissions, which must first be transported to the port of Rotterdam. These transportation, storage, and building costs make CCS an option that is more expensive and efficient when mitigating CO2 emissions (Rubin et al., 2015). *2. Energy-intensive to operate, associated with high emissions* CCS facilities require more energy to capture and compress CO2, reducing the overall efficiency of power facilities. This (partially) undermines the mitigation benefits achieved by CCS (Fu et al., 2023). *3. Long-term storage safety concerns and leakage risks* There is still a possibility of CO2 leakage from storage facilities during earthquakes or geological tremors, as well as the slow deterioration of storage equipment. Extensive monitoring is required to detect leaks using advanced equipment. However, this does not always result in leak prevention. In the event of a leak, massive amounts of CO2 might be discharged back into the atmosphere, causing severe environmental and health consequences. The impact of injection activities on tectonic faults has not been thoroughly studied. Hence, the likelihood of big earthquakes could not be forecast correctly in some situations (Jones et al., 2015). If there is a leak, the environmental benefit may be negated, posing a serious risk to human and animal health. CO2 stored in low-lying places at high concentrations and the capture of deadly hydrogen sulphide would be hazardous to workers and any other organisms in the vicinity (*Carbon Capture and Storage Risks, Explained*, n.d.). *4. Impede the renewable energy transition* Those with a stake in the technology, such as coal and oil firms, as well as other stakeholders involved in the fossil fuel value chain, may use CCS technology to encourage the continued use of fossil fuel-based energy-producing facilities. Furthermore, because of the potential lack of urgency around the switch to renewable energy, the existence of CCS may cause temporal invisibilities among politicians and officials in nations that rely heavily on fossil fuels. It might also be applied to enhance exhausted oil resources, which could provide unfavourable incentives for continuing to extract and consume fossil fuels. Nations already have plans to produce almost twice as much fossil fuel in 2030 as required in a 1.5°C scenario; this would surpass predicted global demand and leave a vacuum that CCS will not close (Rempel et al., n.d.). *5. Limited enabling environment for CCS facilities* There are presently minimal regulatory and institutional frameworks to govern the safe and efficient use of CCS technology (Global CCS Institute, 2020). This can hinder the large-scale implementation of CCS facilities, as companies do not want to invest in unclear regulatory environments. *6. Water Consumption and Environmental Impact*: The CCS process requires high water use, which limits its feasibility in water-scarce countries or regions. Furthermore, the infrastructure required for CCS, such as pipelines and storage facilities, can affect ecosystems and communities, potentially resulting in negative environmental impacts. This high water consumption and the danger of ecological damage make CCS less appealing in locations with low water supplies or where environmental impacts are constantly monitored (IPCC, 2018). *7. Limited Availability of Suitable Storage Sites: * Although the global potential for CO₂ storage is substantial, suitable storage sites are not evenly distributed. In regions that do not have suitable geological formations for long-term storage of captured CO₂, investment in transportation infrastructure will be required which can result in high costs and limit the economic feasibility of introducing this technology (Global CCS Institute, 2021). *8. Risk of CCS Induced Seismicity * They are storing CO₂ underground, typically in deep saline formations or (depleted) oil fields. Introducing CO₂ under high pressure into the rock formations comes with high risks of induced seismicity. These seismic activities, e.g., Earthquakes, can come with safety concerns and the potential to damage the earth's layers above (Chen et al., 2023). Similar to the instability of Fracking, it can influence the people living around the Storage Zones. *9. Public Opposition and NIMBYism: * In general, the public is often concerned about industrial activities whenever they are happening in their local area. This form of local opposition is called NIMBY, which stands for "Not in my backyard." Additionally, some parts of the broader public might fear leakages and other potential risks of CCS. The establishment of storage facilities has faced opposition and protest from people opposed to the technology, especially for projects that are planned close to populated areas (Lipponen et al. 2017). Continued resistance would make implementing the technology at the necessary scale harder. This is why the involvement of stakeholders is already an established part of many CCS projects (Global CCS Institute 2020). **Question 1B: Discuss the following statement: normal science is not appropriate to help solve the question of whether to pursue carbon capture and underground storage to reach net zero targets.** Achieving net zero global emissions has high decision stakes, a large number of stakeholders involved, and high uncertainty. Thus, normal science is not suited as this mode of science considers a single 'question' within a particular field, whereas inter- and trans-disciplinary research will be required to evaluate how best to achieve net zero. Moreover, normative value judgments will be involved in how to transition to net zero, including ethical considerations such as how developing nations should achieve economic and social development while still reducing their emissions. Task 3: Knowledge types and worldviews on CCS --------------------------------------------- **Question 1: What types of knowledge do we see in the general discourse around CCS and how might different worldviews / knowledge types impact the debate around CCS? ** 1. *Knowledge types* There are several different types of knowledge which will inform and direct the debate around the use of Carbon Capture and Storage (CCS) technology to mitigate climate change. Identified knowledge types include: i) interdisciplinary knowledge; ii) technical/scientific knowledge; iii) systems thinking; and iv) experiential/practical knowledge. These knowledge types, and their perspectives on the use of CCS, are elaborated in the following paragraphs. - **Interdisciplinary Knowledge** Within the general discourse of CCS there are many different ways to approach the integration of different types of knowledge (Mazzocchi, 2006). Thus, an interdisciplinary approach is what is needed. To gain all the knowledge necessary one should combine technical, environmental, economic, and also social perspectives. Integrating these different scientific perspectives leads to coproduction of knowledge, which focuses on learning from and with other knowledge types (Van Opstal & Hugé, 2013). For example, Markusson et al. (2011) develop an interdisciplinary framework to assess different dimensions of uncertainty of CCS, including technical, economic, financial, political and societal factors. - **Scientific / Technical Knowledge** According to Van Opstal and Hugé (2013), this is referred to as \"conventional/ neo-positivist scientific assumptions: de-contextualization, fragmentation, privileged, ideological and outside lived experience\" (p.700) that regularly govern conversations about the environment. Tools and procedures known as the scientific method are used to replicate, organize, and direct research to discover the truths. The scientific method allows for some consistency even in the face of its complexity and variety. Thus, scientists can only ascertain \"truths\" about the natural world (2013, p.698). Regarding the CCS, Scientific/technical knowledge is very much needed for the implementation in every part of the process, a.k.a. Capturing and Storage, Transport and Permanent Storage of the CO2. Each method has been researched, tested, and approved scientifically for use in work. Additionally, scientific/technical knowledge used in the measurement, monitoring, verification, accounting, and risk assessment systems can help to reduce or eliminate the risk that stored carbon dioxide poses to humans and the environment(Center for Climate and Energy Solutions, 2023). - **Systems thinking ** Systems thinking is a scientific knowledge type which views reality as composed of multiple, interdependent socio-ecological systems that interact in unique and unpredictable ways to create emergent properties that cannot be discerned from consideration of system components alone (Preiser *et al*., 2018). Rather, systems thinking emphasizes the importance of understanding relations *between* the parts, as well as the parts themselves, to gain a more complete understanding of the system (Fitzpatrick, 2023; Preiser *et al.,* 2018). Systems thinking is particularly useful for taking decisions in complex circumstances with high uncertainty (Fitzpatrick, 2023). This knowledge will provide invaluable insights for the application of CCS, as it enables a holistic consideration of the broader environmental, socio-cultural, economic and technological landscapes which CCS will be introduced into (Van Opstal & Huge, 2013). - **Experiential / Practical Knowledge*** * Experiential knowledge is critical for the successful implementation of Carbon Capture and Storage (CCS) technologies. Fitzpatrick (2016) underscores the importance of this knowledge, which is gained through practice and lived experience (p. 255). Local communities near potential storage sites offer invaluable insights into local geology, hydrology, and environmental factors, essential for informed site selection and risk assessment (Ziegler & Forbes, s.d.). Furthermore, workers in carbon-intensive industries, such as steel and cement manufacturing, possess practical knowledge about industrial processes that is crucial for designing effective carbon capture systems (Nielsen *et al.*, 2022). Communities that have hosted CCS demonstration projects bring firsthand experience regarding the technology\'s impacts and challenges, making them key stakeholders in future developments (Clean Air Task Force, 2020). 2. *Worldviews* It is not possible to identify a concrete number of worldviews which could inform perspectives on CCS; however, we have selected several which would be particularly relevant in the public debate. These include the: i) indigenous worldview; ii) ecological worldview; iii) technocratic/techno-optimistic worldview; iv) expansionist worldview; v) cultural; and vi) religious. - **Indigenous worldview** The Indigenous worldview is defined by Fitzpatrick (2023) as an 'umbrella...term to encompass non-colonized ways of knowing and being from non-migratory ethnic cultures' (p. 21). Indigenous worldviews typically have a holistic perception of the world in which the human-nature relationship is closely intertwined (Mazzochi, 2020). The main tenets of this worldview include reciprocity with, and stewardship over, the natural resources which support an Indigenous community (Mazzochi, 2020). Accordingly, Indigenous worldviews are likely to reject CCS as a sustainable solution to climate change for two main reasons. First, CCS has many potential environmental risks, including the re-release of captured CO2 and increased seismicity (Gholami *et al.*, 2021; Bedle *et al.*, 2024), such that it could not be considered an example of stewardship of nature. Second, CCS has also been critiqued as an example of settler colonialism when introduced into formerly colonized territories, such as the Tar Sands in Alberta, Canada (Alexander and Stanley, 2021). In these instances, CCS can result in the displacement of Indigenous communities as well as the degradation of natural resources which have spiritual significance for these communities. - **Religious worldview** The discourse around CCS demonstrates how different worldviews shape environmental attitudes. Worldviews evolve through the continual interpretation of knowledge, beliefs, values, and standards (Fitzpatrick, 2023). Religious and spiritual viewpoints have a substantial impact on attitudes toward CCS (Fitzpatrick, 2023). For example, Muslim participants frequently regard CCS as ethically problematic, claiming that it disturbs the natural balance and depicts bad stewardship of Allah\'s creation (Hope & Jones, 2014). Similarly, Christians express worries about safety and costs but embrace environmental modification if it enhances human well-being, demonstrating a utilitarian viewpoint (Hope & Jones, 2014). In contrast, secular perspectives promote technical solutions, arguing that CCS is critical to mitigating climate change, even at the expense of natural systems (Hope & Jones, 2014). This technocratic mindset prioritizes practical solutions over ethical concerns. Recognizing these different worldviews is critical for developing CCS communication methods, as religious faith has a substantial impact on public perceptions of technological breakthroughs (Hope & Jones, 2014). - **Ecological worldview** The ecological worldview is opposed to the expansionist worldview. (Van Opstal & Hugé, 2013). It is associated with strong sustainability, which sees environmental protection as precondition for economic development. It assumes that some technologies can substitute and help in the advancement of environmental protection, but imposes that humans still have to shift their behavior and should never rely on technological solutions. At the bottom of this is the precautionary principle, that prescribes to be careful with new technologies if the harm of them has not been fully assessed yet (Baker, 2016). According to the ecological worldview, CCS should not be seen as a solution to climate change. The technical fix of CO2 pollution creates perverse incentives for the continued reliance on fossil fuels. It is important to focus on the risks of CCS, such as potential leakage, to avoid local catastrophes. Within this worldview, a well-researched and proven CCS application might be useful to prevent emissions from hard-to-abate sectors in the upcoming decades, however, at the current point reliance on this technology should be avoided and the focus and investments should be focused more on the prevention of greenhouse gas emissions. - **Technographic or techno-optimistic worldview ** According to the essay by John Danaher, Techno-optimism is a collection of linked viewpoints that differ across multiple dimensions. Techno-optimistic perspectives differ in terms of optimism\'s degree, temporal direction (past, present, or future), epistemic robustness, and the role of technology in maintaining optimism (Danaher, 2022). Furthermore, according to the United Nations Development Program, technology provides an enormous opportunity for addressing global development concerns at scale, and open-source research uncovers development solutions more effectively. However, technology can be incorrectly comforting when it comes to treating the symptoms rather than the root causes of issues like climate change. They elaborate on the idea whether modification does not address the underlying causes of global warming but may relieve some of its symptoms (*Will Techno-optimism Make Us Complacent?*, UNDP). Based on this, a technocratic or techno-optimistic worldview would eventually support CCS. The expansionist worldview closely aligns with weak sustainability, which assumes the substitutability of natural capital with artificial capital and thus would support CCS in mitigating carbon emissions. - **Expansionist Worldviews*** * Expansionist worldviews tend to be prioritizing economic growth and technological solutions over being thoughtful regarding nature. Thus, this worldview would be focussing highly on emphasizing the technical aspects of CCS, seeing that it is beneficial for industries to continue their work but shift their carbon emissions. This in line then also leads to economic considerations regarding the projects being considered. The economic viability and potential impacts on economic growth however should be aligned. As the focus is more on the economic and growth-drive perspective, the expansionist worldview might limit the consideration of ecological impacts, or only be focussing on a short-term perspective regarding the project\'s influence on nature (Van Opstal & Hugé, 2013). - **Cultural Worldviews** Fitzpatrick (2023) within his paper mentions four different world views related to a cultural dimension; Fatalist, Hierarchical, Individualist, and Egalitarianism. These four worldviews contribute to unique perspectives on risk management and decision making as well as a human-nature connectedness (Fitzpatrick, 2023; Bedle, 2024). Individualistic worldviews consider nature to be resilient to human actions. Fatalist worldviews mean that outcomes are unknown, end results cannot be predicted. Hierarchists\' worldviews consider nature to be manageable within certain thresholds. Egalitarians understand nature to be very fragile and to be in a delicate balance with society (Bedle, 2024). Considering these worldviews now in relation to Carbon Capture and Storage different views become clear. Individualistic worldviews would not be supportive over CCS as they do not believe that human actions harm nature, and thus there is no need to implement technological solutions to mitigate carbon emissions. Fatalist worldviews would be accepting over all risk associated with CCS, e.g., seismicity, and be acceptive over all potential outcomes. Egalitarianistic worldviews would be supportive over the implementation of CCS, as they would see it as a necessary step to mitigating climate change; focussing on a broader societal benefit over the consideration of potential localized risks (Bedle, 2024). Nonetheless, there are many more worldviews that could be considered within the topic of CCS and worldviews. Every person, involved or not involved within CCS has a different perspective on it or has different experiences with it. 3. *Conclusion* The debate on the feasibility and sustainability of CCS is influenced by multiple types of knowledge, including scientific/technical, experiential/practical, indigenous/traditional, interdisciplinary, and holistic/systems knowledge. There are also a diverse range of worldviews which influence perspectives on CCS, including the indigenous, ecological, technocratic/techno-optimistic, expansionist, cultural and religious worldviews. Recognizing and integrating diverse knowledge types and worldviews in the CCS debate is essential for developing sustainable and socially acceptable solutions. This requires ongoing dialogue, interdisciplinary collaboration, and inclusive decision-making processes in CCS policy and implementation. Task 4: CCS problems and Hoppe's typology ----------------------------------------- **Question 1: How would you classify the CCS problem according to Hoppe's problem typology and how might this problem type change over time?** According to Hoppe (2018), there are four driving principles as enabling preconditions for a problem-structuring approach to construct a policy design: (1) problem sensitivity, (2) frame reflectiveness, (3) alternating forward and backward mapping, and (4) moving back and forth between puzzling and powering. Hoppe (2018) proposes a problem typology based on the level of certainty in knowledge and agreement on norms, values and goals among different stakeholders (p. 15). Under this typology, the four main types of problems are elaborated below. 1. *Structured problems* display high certainty in knowledge and agreement on norms, values and goals for responding to the problem (Hoppe, 2018, pp. 15). An example of a structured problem is the stratospheric ozone depletion caused by Chlorofluorocarbons (CFCs). Since their invention in 1928, CFCs have become widely used within, for example, air conditioning and refrigerators, because of their effectiveness as a cooling agent (Elkins, 1999). Over time, with an increase in knowledge regarding CFCs, it became clear that they can destroy ozone molecules in the Earth's stratosphere which are necessary for life on Earth (Elkins, 1999). Certainty of knowledge regarding the use of CFCs thus gradually became extremely high as robust evidence of their impact on the ozone layer emerged (Elkins, 1999). This prompted the Montreal Protocol (1987) as part of an international attempt to reach a consensus on goals, norms and values for addressing the depletion of the ozone layer (US Office of Environmental Quality, N.d.). Thus, the use of CFCs evolved into a structured problem, in which certainty of knowledge (regarding their impact on the ozone layer) was high, and agreement on norms, values and goals (regarding how to prevent the CFC-driven depletion of the ozone) was achieved (Hoppe, 2018). 2. *Moderately structured problems* with agreement on ends display high certainty on knowledge (the 'ends') but low agreement on norms, values and goals (Hoppe, 2018, pp. 15). An example of a moderately structured problem (ends) is abortion. From a knowledge or technological side we know how to approach abortions. Nonetheless, the agreement on norms, values and goals greatly differs when considering multiple worldviews. Worldviews are considered a mental model or framework which is the basis for how individuals or groups interpret the world around them (Van Opstal & Hugé, 2013). 3. *Moderately structured problems with agreement on means*, which displays low certainty on knowledge but high agreement on norms, values and goals (the 'means') (Hoppe, 2018, pp. 15). An example of a moderately structured problem (ends) is poverty. Poverty is widely seen as a societal problem, meaning all are agreeing based on norms, values and goals. However, no sufficient solution for the problem has been found yet, thus, there is high uncertainty regarding knowledge to find a general solution to poverty. 4. *Unstructured problems*, in which certainty of knowledge and agreement on norms, values and goals are both low (Hoppe, 2018, p. 15). An example of an unstructured problem is the public debate in the UK that led up to the so-called Brexit vote. Many people wanted to leave the European Union, while many others wanted to remain part of it. The consequences of leaving the EU then were uncertain, as no other country had done it before. As a result of the debate, there was a referendum in 2016 the majority of citizens voted for the UK to leave the EU. Before the vote, it was considered an unstructured problem as it came with high knowledge uncertainty involved, and with different worldviews regarding the decision. Hoppe argues that the key challenge in policy design is \"how to move responsibly from politically intractable, unstructured or less structured policy problem types towards the politically \'tamed\' or structured ones\" (Hoppe, 2018, p.15). **Initial classification (structured problem)** When the CCS technology first emerged in the 1970s, it was intended for enriching depleted oil fields, rather than emissions mitigation, and thus consensus on goals, norms and values was high (Financial Times, 2022). The application of the technology was well understood, such that certainty of knowledge was also high. This was reinforced by low certainty in knowledge regarding the impact of greenhouse gases (GHG) on the earth\'s system at the time and the future impact of the storage. Thus, the use of CCS can be initially considered a structured problem per Hoppe's typology. **Progression from structured to unstructured problem** As climate modelling progressed over the last fifty years, scientific consensus on the impact of anthropogenic greenhouse gas emissions has been reached, with the Sixth Assessment Report of the Intergovernmental Panel on Climate Change concluding that: "It is unequivocal that human influence has warmed the atmosphere, ocean and land. Widespread and rapid changes in the atmosphere, ocean, cryosphere and biosphere have occurred." (IPCC, 2023, p. 46). This development has undermined the certainty of knowledge on the use of CCS, as researchers raised concerns regarding the long-term risks of captured emissions being re-released into the atmosphere (Pietzner et al., 2011). Certainty in knowledge thus decreased. Simultaneously, public debate regarding the safety and efficacy of CCS increased, and considerable disagreement on the norms, values and goals for the application of CCS can be observed, as well as fatal consequences of poor management of the system (Dunne, 2018). Also, the use cases of CCS were extended not only to enhanced oil recovery but towards solving CO2 pollution with the technology. Accordingly, CCS evolved from a structured to an unstructured problem (Hoppe, 2018). **Progression from unstructured to moderately structured problem** In parallel to the development of scientific understanding of climate change, the technical aspects of CCS have also become relatively well understood since its emergence in the 1970s, as has been demonstrated in various projects (Chrysostomidis et al., 2012; SCI, 2024). Accordingly, certainty of knowledge is considered high, particularly in the short term. However, the societal consensus on its use for climate change mitigation continues to be divided. This is evident in debates between advocates of renewable energy and supporters of CCS, especially concerning its application in hard-to-decarbonize sectors like heavy industry (Clean Air Task Force, 2024). There is also still uncertainty about the long-term effects and risks of CCS (SCI, 2024). Thus, the CCS problem can be considered a moderately structured problem with certainty of knowledge (in the short term). However, the consideration of different time scales also highlights the challenges of objectively categorising CCS into a single problem category. **Potential future evolution of the CCS problem** A key development that may influence the structure of the CCS problem is the ratification of the Paris Agreement by 196 UNFCCC member states in 2015, which commits its signatories to limit global warming to between 1.5 and 2C above pre-industrial levels (1850-1900) (UNFCCC, 2016). As part of this commitment, many governments have begun implementing Nationally Determined Contributions (NDCs), which outline their climate mitigation and adaptation contributions to the Paris Agreement (Levin et al., 2023). This commitment to global GHG mitigations may influence agreement on norms, values and goals regarding the use of CCS as part of a climate change mitigation strategy. This may result in the use of CCS transitioning from a moderately structured problem with agreement on ends to a structured problem, as agreement on means (consensus on norms, values and goals) may be reached (Hoppe, 2018). **Conclusion** In conclusion, the use of CCS has evolved from a structured problem when first introduced to an unstructured problem as scientific consensus on the impact of GHG emissions on the earth system was reached. In parallel, the use of CCS continued and the technical understanding of safety and efficacy has developed, such that certainty of knowledge has been achieved, with CCS evolving to a moderately structured problem with agreement on ends today. In the future, international commitments on climate change mitigation as expressed in the Paris Agreement may facilitate greater consensus regarding the use of CCS for climate mitigation, and cause it to transition towards a structured problem. It should be noted that while Hoppe (2018) is useful for framing complex policy problems, problems often do not fit within a single, rigidly-defined category. A problem usually evolves along the direction of more consensus on norms/values or certainty of knowledge, but it is unclear when the point is reached that it changes into another type of problem. It is only possible to concretely state where a problem could be classified after a long time has passed. Task 5: Boundary work in the realm of CCS ----------------------------------------- **Question 1: What would boundary work look like for the Porthos project and how could it help to inform Dutch policymakers about carbon capture and underground storage? ** Boundary work for the Porthos project can inform Dutch policymakers about carbon capture and underground storage (CCS) by facilitating knowledge integration and managing the science-policy interface (Turnhout & Halffman, 2012; Wiegleb & Bruns, 2022). **Understanding Boundary Work in Porthos** The Porthos project will store 37 Mton CO2 off the Dutch coast and involves multiple communities of expertise, including scientists, experts, NGOs, policymakers and judicial systems (Porthos, 2024b; Ranevska, 2024). This trans-disciplinary nature necessitates collaboration across the science-policy boundary to communicate scientific knowledge to policymakers for informed decision-making. *Scientific and Technical Aspects:* Boundary work in Porthos involves demarcating which knowledge types should be used for decisions and defining roles of different organisational units in feasibility assessments and decision-making (Netherlands Court of Audit, 2024). *Economic Considerations:* Economic boundary work has addressed cost implications, with Porthos expected to yield a 2.2% return on infrastructure ownership (Netherlands Court of Audit, 2024). The Court of Audit publication (2024) functioned as a boundary object to communicate opportunities for increasing fiscal revenue from the Porthos project to policy-makers. *Environmental considerations:* Clearly-demarcated rules and regulations informed the evaluation of the environmental impact of the project (Akerboom et al., 2021). An Environmental Impact Assessment (EIA) evaluated t

Summary Sustainability Science PDF

Document Details

Tags

Related

Summary

Full Transcript

Upgrade to continue