Many deltas are increasingly threatened by environmental change, including climate change-induced sea-level rise, land subsidence and reduced sediment delivery. Dealing with these challenges is a pressing necessity because deltas are home to many people and are important centres for economic and agricultural development. Successfully adapting to climate change requires a social-ecological system (SES) perspective, emphasising that social and ecological components of deltas are intertwined. Various modes of governance have been suggested to deal with uncertainty associated with environmental change in SESs, such as adaptive governance. Adaptive governance underlines the need for governance systems to be flexible enough to adapt to variable degrees of uncertainty in SESs. In this paper, we analyse the Dutch Delta Programme (DDP) and the Mekong Delta Plan (MDP) to explore their strengths and limitations relating to nine principles for adaptive governance proposed by DeCaro and others. We evaluate the suitability of this framework for the Rhine and Mekong deltas and contribute to the current understanding of delta governance in light of climate change. Most of the principles outlined by DeCaro and others are present in the DDP and MDP. However, adaptive governance is context dependent. The Rhine and Mekong deltas display different obstacles to adaptive governance, some of which are not sufficiently emphasised in this academic adaptive governance framework. Instead of relying on one framework as a blueprint for adaptive governance, using principles from different frameworks depending on the case may be the best approach for addressing environmental challenges in deltas. Under natural conditions, deltas evolve by the interaction of sediment deposition, redistribution and loss.5 Populations are growing rapidly and urban areas are expanding into deltas. Concurrently, climate change is causing sea levels to rise,6 many major deltas are sinking,7 and sediment delivery to deltas is decreasing.8 These challenges are compounded by issues such as salinity intrusion and waterlogging.9 Although it is uncertain how these environmental challenges will unfold in the future, it is clear that delta drowning could result in the migration and displacement of millions of people, declining food security, biodiversity loss, ecosystem degradation, and loss and damage to the lives and livelihoods of delta inhabitants.10 Adapting to climate change and other environmental challenges in deltas is therefore a pressing and unavoidable necessity.11The current rate of population and environmental change in deltas is higher compared with the global average.12 For deltas to successfully adapt to environmental change, the interdependencies between social and environmental components of deltas must be recognised.13 This requires a social-ecological system (SES) approach to governance, emphasising that social and ecological systems in deltas can no longer be viewed independently but should be seen as strongly coupled.14 Various modes of governance have been put forward for dealing with uncertainty and complexity in dynamic SESs, such as adaptive governance,15 anticipatory governance,16 interactive governance,17 and transformative governance.18Adaptation is not solely shaped by changing climatic and environmental conditions, it is determined by the responses of social systems to those changing conditions.19 Here, we chose an adaptive governance framework because it focuses specifically on the need for governance systems to be flexible enough to adapt to variable degrees of uncertainty and complexity in SESs,20 which is highly relevant for deltas under accelerating environmental change. Furthermore, there is a need for comparative empirical case studies that apply principles for adaptive governance in different social and environmental governance contexts.21 Comparative approaches enable learning and knowledge sharing about the governance of deltas worldwide.22An improved understanding of the governance of deltas is imperative in light of climate change, yet the number of studies that focus explicitly on governance in deltas is relatively limited.23 (DDP) and the 2013 Mekong Delta Plan24 (MDP) — policy documents that function as guidelines for dealing with environmental challenges in the deltas — by analysing them against principles for adaptive governance proposed by DeCaro and others.25 This analytical framework conceptualises the extent to which laws and institutions enhance conditions for self-organisation, flexibility and adaptation, and provides legal and institutional design principles for adaptive governance. The framework is highly suitable for assessing the DDP and MDP, because governing SESs such as deltas involves formal rules (laws) and stakeholders, but also other actors such as businesses, civil society organisations and citizens.26 Based on the application of the adaptive governance framework by DeCaro and others, we aim to assess the suitability of this framework for the Rhine and Mekong deltas specifically and contribute to a better understanding of the governance of deltas generally under climate change.In this paper, we study to what extent overarching delta management plans for the Rhine and Mekong deltas incorporate principles for adaptive governance. We evaluate the strengths and limitations of adaptive governance in the 2020 Dutch Delta ProgrammeIn 2.27 The governance of deltas is complex due to the often international character of their river basins and changing nature of both the ecological and social dimensions.28 Deltas are dynamic SESs characterised by uncertainty and unpredictability. Top-down, centralised governance is not ideal for dealing with uncertainty and unpredictability in SESs, because it often treats SESs as if they are linear, predictable, or even unchanging, resulting in rigid policies and a lack of adaptability.29 Effectively governing deltas requires a shift away from a steady-state view that aims to control and stabilise SESs, assuming gradual changes as the norm and ignoring interactions within and across systems and scales.30One of the most persistent obstacles to the sustainable management of deltas relates to governance.31 Adaptive governance emphasises the need for governance systems to be flexible enough to adapt to feedback from the social and ecological parts of the system it governs.32 Complex problems such as climate change, land subsidence and reduced sediment delivery in deltas embed different interests and perspectives, which have to be considered to generate suitable governance.33 By embracing a broad set of actors, organisations and institutions, adaptive governance aims to create polycentric, multilevel institutions where power and responsibility are shared among government agencies and non-governmental organisations, creating flexible and responsive governance conditions.34 Including a variety of stakeholders can stimulate collaboration and trust building,35 whilst also giving access to different kinds of knowledge vital for social learning and innovation.36 Learning and experimentation are integral to the adaptive capacity of governance systems, because they allow actors to improve their understanding of the SES and adapt their behaviour accordingly.37 However, without leadership showing direction and motivating others, governance systems may be unable to respond to challenges that affect society.38Adaptive governance has been put forward as an alternative governance approach to deal with the uncertainty and unpredictability inherent in SESs.39 Resilience refers to the ability of SESs to change and persist whilst maintaining the same processes and structures .40 Adaptive governance builds on theories of resilience and aims to incorporate feedbacks from changing SESs before they shift into an undesirable state.41 This is important for the long-term sustainability of SESs, if the governance plan revolves around perpetuating the current configuration;42 in other words, if the current state of the SES is still acceptable from a biophysical and stakeholder perspective. Transformative governance has similar factors to adaptive governance, but requires more 43 to push a SES (with human agency) to a new configuration. If conditions in the SES are too degraded, for example, the catastrophic decline of coal mining jobs in West Virginia in the United States, then transformation via transformative governance to a new regime with a different set of processes and structures in the SES  is a more appropriate course of action.44Adaptive governance underlines the importance of managing resilience in SESs to address uncertainty and surprises.3.45 Legal design principles in the framework include reflexive law, legal sunsets, legally binding authority, legally binding responsibility and tangible support. The institutional design principles ‘refer more broadly to features of rule-governed systems, like clearly defined socio-political and geographic boundaries, that help to solve problems collectively’.46 Institutional design principles in the framework include well-defined boundaries, participatory decision-making, internal enforcement and internal conflict resolution. An overview of the principles, as well as their definition and associated key concepts, can be found in The adaptive governance framework by DeCaro and others provides five legal  and four institutional  design principles for adaptive governance. The legal design principles concern ‘(a) elements of official legal systems that determine structure, authority, function, and guidelines for government agencies […] and private centres of activity  and (b) rules and regulatory systems that deal with compliance’.47 This is crucial for adaptive governance, because the inclusion of different stakeholders enhances conditions for flexibility, creativity, innovation and responsiveness needed to adapt governance systems to changes in a SES.48 Internal conflict resolution and internal enforcement generate conditions for fairness and legitimacy, essential to cultivate trust among the participating stakeholders. Technical, financial and informational support provide public and private stakeholders with sufficient resources to successfully implement adaptive measures;49 while principles such as reflexive law and legal sunsets stimulate decision-making that is flexible, iterative and open to revision, encouraging learning required to deal with the uncertain, complex and ever-changing nature of SESs.50The legal and institutional design principles create conditions for the emergence of adaptive governance within society. Principles such as participatory decision-making, legally binding authority and responsibility aim to create favourable conditions for the emergence of partnerships and compacts between stakeholders.4.4.1.We conducted a content analysis to explore the strengths and limitations of the 2020 Dutch Delta Programme (DDP) and the 2013 Mekong Delta Plan (MDP) by analysing them against principles for adaptive governance formulated by DeCaro and others. Content analyses are useful research tools to determine the presence of, e.g., principles within qualitative data, and to analyse the meanings of these principles. The DDP and MDP are policy documents that function as guidelines for dealing with climate change, sea-level rise and other environmental challenges such as subsidence, underlining the relevance of these policy documents for this analysis. Comparing the Rhine and Mekong deltas also provides insights from different global north-south contexts. In 4.1.1.2, making it the largest delta in Europe.51 The majority of the delta is located in the Netherlands. Most of the Dutch population is concentrated in the coastal lowlands of the Rhine delta52 and the area is of great economic importance with 65% of the Dutch gross national product being generated here. Protecting the delta’s inhabitants, their livelihoods, infrastructure, etc. is therefore of critical importance, which explains why the Rhine delta is heavily engineered and shaped by human interventions.53 Most of the damming of the delta was initiated by destructive floods in 1953, which also led to the formulation of the first DDP committed to improving flood protection systems in the Netherlands.54 Although the increasing number of dams and embankments reduced the occurrence of floods, it also led to a number of environmental challenges. Reduced sediment deposition on the delta plain, combined with drainage and land reclamation, resulted in land subsidence.55 This is compounded by sea-level rise, which increases the delta’s vulnerability to flooding and storm surges, saltwater intrusion and increases the risk of permanent inundation. Furthermore, there is no guarantee that embankments will be sufficient to protect the Rhine delta and its inhabitants from sea-level rise.56The Rhine-Meuse-Scheldt delta  is located in the south-eastern corner of the North Sea Basin. The low-lying delta plain is 25,347 km57 The DDP establishes and reports on the progress on five Delta Decisions,58 which contain plans to protect the Netherlands from flooding and water shortages. The Delta Decisions form the basis of Dutch water policy,59 and are anchored in the National Water Plan,60 which describes the general aim and direction of water policy in the Netherlands, in the Water Act61 and in administrative agreements with local governments. Delta Decisions provide long-term, strategic policy objectives, to be further developed and implemented in relevant policy domains. The DDP is structured around three main themes: water safety (mainly flood protection and flood risk management), freshwater and spatial adaptation. The measures proposed in the DDP are mostly financed through the Delta Fund. Every year, the Delta Commissioner presents the new DDP to the Minister of Infrastructure and Water Management, who is ultimately responsible for the development of and progress on the DDP. The Delta Commissioner promotes the implementation of the DDP and monitors its progress.62The DDP is a policy document from the Dutch government to protect the Netherlands against increasing flood risks resulting from climate change. It is an initiative in which the Dutch government works together with provinces, municipalities, regional water authorities, civil society organisations, research institutes, businesses and citizens.4.1.2.2, of which 52,100 km2 are located in Vietnam.63 The Mekong delta is the largest delta in Southeast Asia and the third largest delta in the world, home to about 20 million people with a population density of up to 500 persons per km2.64 The area is of great economic importance. It provides 50% of Vietnam’s food production and more than 200 million people rely on the delta for food.65 However, anthropogenic activities are threatening the sustainability of the Mekong delta. The construction of dams in upstream countries reduces sediment supply to the delta, exacerbating delta shoreline erosion.66 Embankments have been constructed to deal with increasing flood risks and the destabilising of channel banks, but these changes have their own negative impacts such as increasing river flow velocities causing flood hazards and erosion.67 Furthermore, the Mekong delta is sinking rapidly, which, combined with sea-level rise, increases the delta’s vulnerability to flooding and storm surges, saltwater intrusion, salinisation of groundwater, coastal erosion and inundation.68 This significantly threatens the lives and livelihoods of the delta’s inhabitants as well as its agricultural productivity.The low-lying Mekong delta is approximately 62,520 km69 The MDP aims to respond to the consequences of climate change and to ensure sustainable socioeconomic development of the Mekong delta. In Vietnam, partners in developing the MDP included the Ministry of Natural Resources and Environment and the Ministry of Agriculture and Rural Development. Dutch partners included the Dutch government, Wageningen University, Deltares (a knowledge institute), Rebel (a management consultancy organisation), and Royal HaskoningDHV and Water.nl (consultancy firms). The MDP is presented as a reference document for Vietnamese government agencies and organisations at all levels. It functions as a tool to support the review, coordination and integration of current policies for the delta as well as a guideline for the implementation of these policies.70To deal with these environmental challenges, the MDP was created together by the Socialist Republic of Vietnam and the Kingdom of the Netherlands under the Strategic Partnership Arrangement on Climate Change Adaptation and Water Management Arrangement.4.1.3.71 and Delta Act.72 The Water Act brings together a range of laws on water management and flood protection. It is legally defined that a DDP must be published every year. The Minister of Infrastructure and Water Management presents the DDP to the Parliament together with the budget for the Delta Fund for the new year.73 The MDP, on the other hand, has no formal status in the Vietnamese legal system but rather provides strategic advice to the government of Vietnam.74 However, the principles and strategies proposed in the MDP were embraced by Government Resolution 120, which provides policy guidance on ‘Sustainable and Climate-Resilient Development of the Mekong Delta of Vietnam’.75 An action plan to further implement Resolution 120 was issued in 2019 with Decision 417.76 In addition, a new plan for the sustainable development of the Mekong delta has recently been launched: the Mekong Delta Regional Master Plan,77 which can be seen as a successor to the MDP.The DDP and MDP are similar in that they both describe and propose strategies to protect the deltas against flooding. The documents provide guidelines to deal with uncertainty regarding climate change and other environmental challenges such as reduced sediment delivery. However, the documents differ in their legal basis. The formulation of the DDP is prescribed in the Dutch Water Act4.2.For each of the nine design principles provided in the adaptive governance framework by DeCaro and others, key concepts were identified which were used to assess the DPP and MDP . Some ofThe principles that were found to be present in the DDP and the MDP were further assessed based on their clarity and frequency. Clarity refers to whether a principle is mentioned explicitly or implicitly in the documents. If the documents specifically mentioned the key concepts associated with a principle, that principle was assumed to be explicitly present for this analysis. If the documents referred to a principle without mentioning the associated key concepts, that principle was assumed to be implicitly present for this analysis  while others were mentioned frequently (> five times). We interpret the frequency of mentions of a principle to reflect its perceived importance by contributors to the DDP and MDP and potentially to decision-makers using the plan. However, we acknowledge that, from a legal perspective, a single stipulation of any obligation can provide strength and binding effect.5.In general, there is good coverage of adaptive governance principles in the DDP and MDP. Most of the adaptive governance principles presented by DeCaro and others are present in the documents, to varying degrees of clarity and frequency ; Table 25.1.The DDP and MDP score well on three of the nine adaptive governance principles provided by DeCaro and others. Both delta plans emphasise tangible support, well-defined boundaries and participatory decision-making. These principles are explicitly present and mentioned frequently in the documents . We argu5.1.1.100 Some examples of tangible support in the DDP and MDP are provided below.Examples of tangible support for stakeholders are frequently provided throughout the DDP and MDP. Tangible support underlines that stakeholders need proper support in the form of, e.g., funds, technology, information, or training from central and local governments to successfully participate in the design and implementation of decisions.101 Due to an amendment to the Water Act, which came into effect on 1 January 2021, financial contributions from the Delta Fund can now also be granted to decentralised authorities, to aid them in implementing measures against coastal, fluvial and pluvial flooding.102 Furthermore, the Dutch Minister for Infrastructure and Water Management made extra funds available for stimulating and facilitating climate change adaptation, meant for pilot projects, knowledge development and knowledge sharing through an online platform.103 The knowledge platform supports the Dutch National Climate Adaptation Strategy and the plan on spatial adaptation in the DDP, and provides information to  governments, businesses and civil society organisations to aid in planning for a climate-proof, water-resilient future.104 In the MDP, an example of tangible support in the form of funds can be found in the proposed flood management measures for the delta. For each of these measures, it is clearly indicated how the measures can be financially supported by the central government.105 Furthermore, the MDP underlines the importance of support in the form of information. The MDP stipulates that the central government should produce a joint knowledge agenda to increase the availability and accessibility of data and information for all stakeholders in the Mekong delta, to aid them in participating in the implementation of measures for a sustainable Mekong delta.106The DDP explicitly states that the Delta Programme is financially supported by the Delta Fund. ‘The Delta Fund holds the financial resources to finance investments in water safety, freshwater and water quality, and the central government’s management and maintenance activities that pertain to this.’5.1.2.107 The DDP and MDP provide multiple examples of agreements about socio-political or ecosystem boundaries that support the principle of well-defined boundaries.According to the principle of well-defined boundaries, socio-political and ecosystem boundaries of an environmental dilemma should be recognised and well-defined.108 In addition, various international collaborations are mentioned in the document, such as the International Commission for the Protection of the Rhine, the Working Group High Water (for the protection of the Lower Rhine), the International Scheldt Commission (for the sustainable management of the Scheldt river) and the Flemish-Dutch Scheldt Commission (for the sustainable management of the Scheldt estuary).109 Similarly, the MDP underlines that ‘arrangements for flood control, securing of adequate freshwater supplies in the dry season, salinity intrusion, regulation and management of an adequate and healthy brackish water zone for aquaculture, coastal defence, etc. are all typically measures that need to be considered at the delta level, [but] in their impact and influences they go beyond the boundaries of local governance and policy jurisdiction’.110 Towards that end, the MDP distinguishes between the upper, middle and lower reaches of the delta and recognises that different parts of the delta need different strategies. The development and implementation of these strategies requires stakeholder collaboration.The DDP clearly acknowledges the international character of rivers and that measures taken in the upstream river will impact the lower delta and vice versa. The Rhine, Meuse and Scheldt rivers all cross international borders, as well as the borders of water management authorities in the Netherlands. This requires collaboration between different administrative entities within the Netherlands and with international stakeholders. For example, the DDP refers to the Policy Platform Water Safety, in which Dutch stakeholders collaborate on water safety issues, including the central government, provincial and local governments, and regional water authorities.5.1.3.111 The DDP and MDP provide numerous illustrations of processes and methods that enable stakeholder participation. For example, the DDP is the product of the collaboration between the central government, provinces, municipalities and regional water authorities, and the Programme is based on ‘input from civil society organisations, knowledge institutes, citizens, and businesses’.112 The DDP also distinguishes between five levels of participation from which the appropriate level can be chosen depending on the specific project. This participation ladder includes ‘informing, consulting, advising, co-producing and (co-)decision-making’.113 Similarly, various knowledge institutes were included in the preparatory phase of establishing the MDP. Stakeholders are also consulted in the implementation of the proposed strategies in the MDP, including ‘experts and specialists from different sectors with a bird’s-eye view across the sectors, decision-makers of local, provincial, and national authorities, [and] representatives from organisations for e.g., industry, fishery, transport, agri- and aquaculture’.114Participatory decision-making prescribes that affected local and regional stakeholders should be able to influence the design and implementation of decisions.5.2.All adaptive governance principles from the framework by DeCaro and others are present in the DDP. In the MDP, on the other hand, two principles are currently absent: the legal design principle of reflexive law and the institutional design principle of internal enforcement ; Table 2115 By emphasising standards and general principles, decision-makers at local levels of government have legal guidance but also flexibility when they need to decide on local issues. The MDP does not refer to reflexive principles. This is a limitation for the emergence of adaptive governance in the Mekong delta, because reflexive principles allow for flexible decision-making at lower levels of government needed to deal with complex and multi-scale SES dynamics.116The definition of reflexive law provided by DeCaro and others prescribes that laws should define procedural norms and establish ground rules, instead of focussing on specific outcomes as the ultimate result.117 However, internal enforcement is needed to monitor the behaviour of participating stakeholders in the governance of SESs and enforce their compliance with rules.118 Monitoring also makes those who do not comply with rules visible to the community, which, in turn, increases the effectiveness of rule enforcement mechanisms.119 Currently, the MDP does not provide illustrations of mechanisms to monitor the behaviour of participating stakeholders and enforce their compliance, a second limitation for the emergence of adaptive governance in the Mekong delta.The principle of internal enforcement is also missing from the MDP. Stakeholder participation creates conditions for creativity, innovation and knowledge sharing which is essential for flexibly governing SESs.6.6.1.The analyses of the DDP and MDP reveal strengths of these delta plans along the same three adaptive governance principles: tangible support, well-defined boundaries and participatory decision-making.120 Furthermore, stakeholder participation can enhance flexibility and responsiveness in decision-making. Decentralising decision-making authority and responsibility can stimulate fast responses to changing local conditions121 , because local and regional stakeholders have easy access to information about local issues and are the first to know when conditions change.122 This is highly relevant for deltas, as governance systems have to deal with a multitude of uncertainties. Participatory decision-making also underlines the importance of stakeholder participation for consensus building and long-term social learning in the governance of dynamic SES123 which can contribute to the sustainability of the Rhine and Mekong deltas.Participatory decision-making is essential for the emergence of adaptive governance. Different stakeholders have access to different types of information, knowledge and expertise, creating innovative and creative governance conditions needed to deal with the complex nature of deltas.124 The emphasis of the DDP and MDP on tangible support would ensure that stakeholders receive adequate  support to participate in decision-making processes and implementation of strategies. Tangible support may also stimulate experimental processes as stakeholders are incentivised to implement their own strategies. Implementation of a variety of strategies by diverse stakeholders may contribute to social learning about the SES being governed and is a central aspect of adaptive governance.125 Without sufficient support, stakeholders cannot perform their tasks satisfactorily,126 which can result in adaptive and cooperative failures.127 Furthermore, the strengths of the DDP and MDP also lie in the emphasis on well-defined boundaries. Working together with a variety of local, regional, national and international stakeholders to govern complex SESs such as deltas can be confusing without a clarification of the jurisdictions of these stakeholders. Well-defined socio-political and ecosystem boundaries facilitate collaboration between different stakeholders and collective problem solving.128However, the participation of local governments, businesses, civil society organisations and citizens in the governance of deltas can be difficult without adequate and sufficient resources.129 During the Middle Ages, many low-lying areas were reclaimed from bodies of water and subsequently protected by dikes against flooding. Windmills were used to pump water from the polders. Maintaining this system required cooperation between a variety of stakeholders.130 Therefore, the principle of participatory decision-making is arguably rooted in Dutch tradition and culture. However, such cultural roots may not be mirrored in Vietnam. The principles of tangible support and well-defined boundaries, which are also emphasised in the two plans, are arguably related to stakeholder participation. Tangible support underlines that stakeholders need sufficient resources to participate, and well-defined boundaries are needed to clarify the jurisdictions of these stakeholders, facilitating cooperation and collective problem solving.131 However, the amount of support available and the jurisdictions of stakeholders are likely to differ substantially between the Rhine and Mekong deltas.The similarities in strengths in the DDP and MDP are not surprising, given that the MDP was created through a collaboration between the Vietnamese and Dutch governments. While we argue these are strengths potentially enabling and encouraging adaptive governance to deal with accelerating and uncertain environmental change, transfer of knowledge and policy from the Rhine delta to the Mekong delta should be done with caution given the contextual differences in environment, culture, politics, law and economics between these deltas. Take, for example, participatory decision-making: water governance in the Netherlands traditionally was based on cooperation and consensus building among stakeholders, called the polder model.In general, the legal dimensions of the adaptive governance framework by DeCaro and others are somewhat underrepresented in the DDP and MDP . Althoug6.2.132 Successful adaptation in SESs often emerges from collaborative, creative processes initiated by governmental and nongovernmental stakeholders.133 The adaptive governance framework by DeCaro and others used in this paper focuses specifically on aspects of laws and institutions that support governing for resilience in SESs. A considerable strength of the framework is that it provides guidance for legal and institutional design that can foster the emergence of adaptive governance. The framework also allows for comparing different cases which may enable learning and knowledge sharing about adaptive governance in different locations.Adaptive governance is a useful framework for dealing with the complexity and uncertainty resulting from accelerating environmental change in SESs.134 DeCaro and others underline this and emphasise that, although supporting more of the legal and institutional design principles can lead to the emergence of adaptive governance, the framework is a working hypothesis. It is crucial, according to the authors, to investigate how the ideas from the framework translate to local cases.135 From the application of the framework to the Rhine and Mekong cases, we found that both deltas experience different obstacles to the emergence of adaptive forms of governance and require an emphasis on different adaptive governance principles. We further elaborate on these obstacles for both deltas in the paragraphs below.However, adaptive governance is context dependent. It is often not the same in two places because it develops within the context of a specific SES.136 Participation of the public in agenda-setting, decision-making, implementation and evaluation of flood measures137 remains limited. In the Netherlands, citizen involvement generally occurs in the form of consultation instead of partnerships.138 This contrasts with other European countries such as the UK and Belgium where flood risks are increasingly considered as a shared responsibility between water managers, other governmental actors and citizens.139The DDP emphasises the principle of participatory decision-making. As already mentioned, this could be explained by the fact that water governance in the Netherlands traditionally was a shared responsibility of various stakeholders. Legally binding authority and responsibility are also present in the DDP, indicating that stakeholders have the authority to make decisions, implement chosen solutions, and are held responsible for their actions (or inaction). However, today, water management in the Netherlands remains mainly a state task as flood risks are so high that they are considered a vital threat to the habitability of the country.140 demonstrate through a social network analysis that many nongovernmental actors in the Netherlands are not seen as equal partners in the governance of water resources. Non-governmental actors remain highly dependent on the main governmental organisation which obstructs the shift from a dominant government towards collaborative, polycentric forms of governance in the Netherlands. Polycentric systems are a key component of adaptive governance and are defined as complex, adaptive systems without a central authority dominating the processes and structures of the system.141 For adaptive governance to emerge in the Netherlands, governmental organisations need to recognise non-governmental actors as equal partners.142 With equal partnerships, we mean that stakeholders do not stand in a hierarchical relationship to each other.143 This may result in responsive and flexible governance systems capable of dealing with increasing levels of change and uncertainty.144 Individual or community-based actions, which could be considered as an addition to public flood defence, are often small-scale measures which can be implemented and revised relatively quickly,145 and would therefore contribute to the adaptive capacity of the Rhine delta. Individual or community-based actions could be stimulated by public authorities by allocating adequate resources to water management partners, such as financial support targeted at the most vulnerable groups.146Fliervoet and others147 found multiple obstacles to the emergence of adaptive governance in the Mekong delta, some of which are also addressed in the framework by DeCaro and others. For example, they found limitations in vertical and horizontal integration, which is also the case in the Rhine delta,148 and in public participation. Vertical integration refers to a well-balanced distribution of responsibilities and authorities and the involvement of lower-level actors in higher levels of decision-making.149 This is addressed in the adaptive governance framework by DeCaro and others with the principles of legally binding authority and responsibility. Ha and others also found limitations in public participation, which refers to provisions that support the participation of stakeholders in the formulation of rules that affect them,150 which aligns with the principle of participatory decision-making in the framework used in this paper. The overlap between some of the obstacles identified by Ha and others with the design principles for adaptive governance in the framework by DeCaro and others highlights the relevance of the design principles for the Mekong delta. To overcome the obstacles in vertical and horizontal integration and public participation,151 the principles of legally binding responsibility and authority, and participatory decision-making, which are present in the MDP, should be more clearly and concretely implemented in Mekong delta governance.Ha and others152 Considering policies and management actions as small-scale experiments is important for adaptive governance, because it provides information about how the system responds to policy and management actions, and thus provides an opportunity to learn about the functioning of a SES.153 It could be argued that policy experimentation is a type of reflexive component, emphasised by DeCaro and others through the principle of reflexive law. However, the definition of this principle by DeCaro and others does not include experimentation specifically, which may be an oversight that is slightly detrimental to the framework.Ha and others also identified factors that obstruct the emergence of adaptive governance in the Mekong delta that are not sufficiently emphasised in the framework by DeCaro and others used in this paper. For example, lack of small-scale policy experimentation is identified as an obstacle to adaptive governance in the Mekong delta.154 This is problematic. Active knowledge sharing can help empower social groups to participate in governmental decision-making processes.155 Although the framework by DeCaro and others discusses the importance of tangible support for stakeholders in the form of funds, technology, information, or training, a focus on active knowledge and information management is absent. Important for the emergence of adaptive governance in the Mekong delta is collaborative knowledge production, diversity in knowledge, and knowledge and information sharing.156Another barrier to the implementation of adaptive governance in the Mekong delta relates to knowledge sharing. Ha and others found that knowledge is not proactively shared by the government with the public.6.3.157 Hence, the adaptive governance principles by DeCaro and others should be used as a generalised guideline and if principles do not directly translate to a particular SES, they should be adapted to the case at hand. Furthermore, other principles that enable adaptive forms of governance, such as those identified by Ha and others for the Mekong delta, should perhaps be added for particular contexts.The above discussion shows that adaptive governance faces different obstacles in the Rhine and Mekong deltas, underlining that adaptive governance is context dependent. Assuming a set of fixed design principles for adaptive governance and applying them to different deltas worldwide is difficult, because it prevents us from seeing the full range of governance problems and potential solutions. Enabling conditions for adaptive governance are context-specific and design principles need to be adapted depending on the local situation. This is important, because governance of climate change and environmental challenges is more likely to be successful when the governance system is adjusted to the SES it tries to manage.6.4.The methods employed in this paper have some limitations which we briefly discuss here. We acknowledge that we analysed a limited number of policies: the 2020 DDP and the 2013 MDP. In addition, the DDP is an ongoing development. It is updated yearly and each DDP builds upon the previous year. We chose the DDP from 2020 because this was the most recent DDP at the time of the analysis. The results in this paper may have been slightly different if the analysis were to be applied to a more recent DDP, e.g., the 2022 DDP, as currently more information and knowledge may be available on the progress and pace of environmental change, as well as on how to deal with these challenges in an adaptive way. However, it is unlikely that the 2022 DDP will differ significantly from the 2020 DDP, as the policy changes incrementally over time. To fully respect the gradually evolving and iterative nature of the DDP, future research should analyse a series of DDPs. However, systematic reassessments of the DDP do not take place yearly but once every six years. Hence, we expect minor differences between the analysis of the 2020 DDP and those published thereafter. The MDP, in contrast to the DDP, was published once, in 2013, and functions as a guideline for the Vietnamese government.Another limitation of the work is that the framework used in this study provides legal and institutional design principles for adaptive governance; however the DDP and MDP are policy documents. This has impacted our results as the legal design principles offered by DeCaro and others are somewhat underrepresented in the DDP and MDP. As mentioned, we acknowledge that the frequency or clarity of the legal design principles in the DDP and MDP do not determine the legal space for adaptive governance. Rather, in this paper we aim to provide an introductory analysis of overarching delta management plans to understand enabling conditions for the emergence of adaptive governance in deltas. However, for future research, we recommend a systematic approach that assesses policy plans, institutional documents and legal instruments, as well as stakeholder interviews and surveys, to better capture the governance context in the Rhine and Mekong deltas. Finally, it is also important to consider that we compared a developed country (Netherlands) with a developing country (Vietnam).7.Adaptive governance has been put forward as a framework for addressing uncertainty and unpredictability in complex SESs such as deltas. We applied an adaptive governance framework to overarching delta management plans for the Rhine and Mekong deltas, the DDP and MDP respectively, demonstrating that most of the design principles for adaptive governance offered by DeCaro and others are present. The strengths of both delta plans regarding adaptive governance lie in the emphasis on tangible support, well-defined boundaries and participatory decision-making. However, the application of the framework to the Rhine and Mekong deltas also shows that governance is likely to need to be adapted to the local context. In both deltas, there are case-specific conditions that obstruct the emergence of adaptive governance, conditions which may be insufficiently covered in the framework by DeCaro and others. Adaptive governance of SESs is often not the same in two places. Therefore, governance principles such as those suggested by DeCaro and others should be seen as a guideline and modified depending on the local situation.158 Detailed analyses of legal and governance structures is important to improve our understanding of the enabling and constraining conditions for adaptive governance of deltas.159 In addition, future research should consider the limitations of this study and account for them in further studies, e.g., by incorporating a variety of legal and institutional documents, as well as further research on transformative governance.Our understanding of the governance of deltas under accelerating environmental change is still limited. As governance systems are extremely important in generating adaptive capacity of a region to environmental change, we recommend systematic, detailed analyses of legal and governance structures in the Rhine and Mekong deltas, but also in other deltas across the globe.


As China’s economic growth slows, green development becomes greatly significant for the high-quality development of the economy. Based on China’s provincial panel data from 2005 to 2021, this study applies fixed effects model and mediating effect model to explore the influence of the government environmental investment on green development. The results indicate that (1) the government environmental investment was conducive to green development, but such effect weakened with time. (2) The government environmental investment indirectly promoted green development through the application of green patents and economic agglomeration. (3) The promotional effect of the government environmental investment varied according to region and time. Specifically, investment exerted the most significant effect on the green development of eastern China, which became more evident after 2015. The government should promote green development by implementing long-term assessment and accountability mechanisms, expanding the scale of economic agglomeration, improving the efficiency of the application of green patents, and improving the accuracy of environmental investment.The  However, green development is systematic and complex, requiring the government’s active involvement in macro control. Environmental investment is an essential way for local governments to implement green development. It provides a solid material financial guarantee for green development and demonstrates the dual functions of promoting the transformation of industrial structure and regulating environmental resources. Although financial departments have been devoted to environmental protection in recent years, the situation remains severe. According to 2022 data from the Ministry of Finance , the national general public budget expenditure exceeded 26 trillion yuan, of which environmental protection expenditure amounted to 539.6 billion yuan, while China’s ecological environment in terms of soil, water, and ecosystems still needs improvement. Therefore, further clarification of the relationship between the government environmental investment (GEI) and green development as well as the acting mechanism is needed in the current context. In summary, exploring strategies to better promote positive interactions between the two under increasingly prominent environmental pressures has become a crucial issue that urgently requires a clear response from relevant academic research, which is of great significance for promoting China’s high-quality economic development.A favorable ecological environment is not only a vital prerequisite for human society to achieve sustainable development but also a realistic demand for people at a certain level of economic development. The factor-driven extensive growth model, which operates at the expense of the environment, is no longer suitable for China’s development in the new era. Therefore, the Therefore, this study takes 30 provinces in China as the research objective and utilizes their data from to 2005–2021 to explore the relationship between the GEI and green development as well as the mediating role of the green patent applications and economic agglomeration. It also investigates the effects of regional and temporal heterogeneity. Reasons for choosing provinces of China as the research objective may lie in two aspects. Firstly, given the vast territory of China, the overall study of GEI is more representative and comprehensive. Study only limited to certain area may be overly concrete and lacks applicability. Secondly, the environmental protection pressure of China remains heavy and the government environmental investment keeps growing. China’s economic growth has historically come at the expense of the environment, which has resulted in significant pollution and issues. Therefore, it is so vital to safeguard the environment that governments have made significant financial investments to support high-quality economic development while protecting environment. By doing so, this study aims to uncover the role of GEI, to further clarify its impact mechanism on green development and provide a theoretical basis and empirical support for the Chinese government to better understand the promotional effect of financial investment on green development and narrow the green development gap between targeted regions.The main contributions of this study are as follows: First, it focuses on the economic effects of the GEI and its relationship with green development, which helps to supplement the literature on GEI as well as its impact and efficiency. Furthermore, this study analyzes the impact channels of GEI on green development from the perspectives of the application of green patents and economic agglomeration. This is conducive to a more comprehensive understanding of the relationship between government environmental investment and green development. It also interprets this impact mechanism from both theoretical and empirical perspectives, compensating for deficiencies in the existing literature to a certain extent. Finally, this study clarifies the existence of regional heterogeneity in the role of the GEI in promoting green development. Based on this, strategies to promote green development are proposed, which are helpful in providing relevant policy references for narrowing regional differences and alleviating the imbalance of regional green development. As the Chinese government is paying increasing attention to the ecological environment, giving full play to the positive role of the GEI in promoting green development and enhancing the positive interaction between them in the targeted regions will further improve the high-quality development of China’s economy.Domestic and foreign scholars have conducted many studies on GEI and green development, which are summarized as follows:The first category concerns green development and primarily focuses on its influencing factors and the government’s policy-guiding role. Initially, scholars focus on the Chinese government’s policy guidance in promoting green economic development. Green financial policies such as green credit can leverage private capital into green development, which broadens the financing channels of enterprises, enhances their green productivity, and reduces carbon emissions, thus promoting the green transformation of the economy –3. MoreoThe second category is the study of the GEI, which is generally considered to be environmentally effective and affected by several factors. First, GEI is conducive to government enforcement restricting carbon emissions and reducing haze through technological innovation , 12. BasFinally, research on the relationship between government environmental investment and green development is the third category. Few in-depth analyses are available on this topic because current studies mainly concentrate on the relationship between the government’s other economic actions and green development. GEI significantly impacts the green economy. Increasing the proportion of fiscal environmental protection expenditures to total fiscal expenditures positively contributes to green technological progress and economic growth . WhetherThe literature review indicates that domestic and foreign scholars have conducted much in-depth research on green development and GEI, providing valuable references for this study. However, gaps remain in the literature. First, existing studies have primarily focused on the environmental effects of the GEI, with insufficient attention paid to its economic effects. Second, existing studies concentrate on the relationship between environmental regulation and the government’s other economic behavior and green development but fail to deeply explore the relationship between green development and GEI, which is closely related to the environment. Moreover, the impact mechanism between the two requires further analysis. Based on this, China’s provincial panel data from to 2005–2021 was used in this study to analyze the relationship between GEI and green development, as well as its impact mechanism, to provide theoretical support to further strengthen the positive interaction between the two and boost the green transformation of China’s economy.Regional green development focuses on resolving existing environmental pollution issues, developing and applying green technologies, and improving and constructing environmental infrastructure. Environmental pollution issues are mainly addressed as negative external effects through taxation or subsidies, internalization of external effects, and the determination of property rights . This reHypothesis 1: GEI promotes green development.Green technology is a key factor in China’s establishment of an ecological civilization and high-quality green development. An essential strategy for driving green technology is to promote and utilize innovative green patents that are oriented towards environmental protection. However, their practical applications involve a complicated and ongoing commercialization process from product R&D to pilot testing to industrialization that spans a significant amount of time. Financial investment must be increased, particularly in the pilot stage, to fully offset capital losses in crucial links. In addition, traditional financial institutions and private capital show less interest in investment because of the high level of uncertainty concerning the application of green patents. In this situation, government finance, as an essential tool for adjusting investment structures, plays a more significant political role than economical, primarily by fostering an environment in which the application of green patents is promoted . The govHypothesis 2: GEI promotes green development through the application of green patents.This study explains three aspects of the effect of economic agglomeration on the relationship between the GEI and green development: competition, scale, and innovation effects. First, the competitive effect is analyzed. Economic agglomeration can encourage the concentration of local businesses and industries in particular areas, which causes rivalry among different producers in terms of price and quality. This competitive effect can constitute an efficient energy-saving incentive mechanism, driving businesses to update energy-intensive technologies and equipment impulsively in response to rising traditional energy prices and more stringent environmental laws . In addiHypothesis 3: GEI promotes green development through economic agglomeration.i and t represent the time and provinces of all samples. gtfp is the green development level, the explained variable of this paper. rein represents the GEI, the explanatory variable of this paper. Control variables that may influence the green development are included. ind reflects the industry proportion of each province. urb is the urbanization level of these provinces. Pgdp and rd represent the economic development level and R&D investment of companies of each province. Ε is the random error term. Β1 demonstrates the effect of government environmental investment on green development. If β1 remains greater than 0 when all control variables are unchanged, GEI is conducive to green development. If not, such investment does not affect green growth.The following benchmark model was created to examine the first hypothesis about the impact of the GEI on green development.mediator represents two variables including the application of green patents (pau) and economic agglomeration (ene). Other variables are the same as in α1 is significantly positive would the Eqs . Referring to Li [The explained variable was the green development level . To improve comparability and eliminate differences caused by various population, this paper measured GEI by the ratio of government environmental investment to the population with reference to Cao et al. [The explanatory variable in this study is the government environmental investment . The higher the industry share in the economy, the stronger is the dependence on traditional energy sources, which, in turn, affects green development. This was calculated using the ratio of industrial added value to GDP. (2) Level of urbanization (urb). Urbanization facilitates factor mobility and resource allocation, which influence green development. This was calculated using the urbanization rate. (3) Economic development (pgdp). The level of regional economic development is closely related to green development; however, simple economic growth at the cost of energy consumption may not fully reflect green economic growth. Therefore, it was evaluated by the per capita gross domestic product and presented in logarithmic form. (4) R&D investments (rd). Technological progress helps enterprises improve their technological levels and energy efficiency, thereby reducing pollutant emissions. This is measured as the ratio of internal R&D expenditure to GDP in industrial enterprises and presented in logarithmic form.Control variables are as follows. Referring to previous research –32, thispau). Considering that this study mainly examines the mediating effect of green patent applications, it uses the number of authorized green patents per 10,000 people as a measurement, with reference to Zhang and Hu [ene). Restricted by the data, this study, referring to Zhang [Mediators are listed below. According to prior investigations, the application of green patents and economic agglomeration are two ways in which the GEI may contribute to green development. Consequently, in this study, two indicators were chosen as mediators. (1) Application of green patents . However, Tibet was excluded owing to missing data. Therefore, the sample data for this study include 30 provinces in China from to 2005–2021. In addition, the data was subdivided into eastern, central, and western regions . In order to reduce the effect of spurious outliers, this article winsorized all variables at 1%. The descriptive statistics of each variable are shown in Based on the The empirical test was conducted in three steps. First, the hypothesis that GEI encourages green development was examined. The second step was to gauge the reliability of this estimation. The purpose of the final section was to test the main thesis, which states that GEI promotes green development through the application of green patent and economic agglomeration. This study adopted a robust standard error in the regression analysis and conducted autocorrelation testing for the variables to address potential heteroscedasticity. The autocorrelation test results showed that there was a correlation between variables, but no significant multicollinearity because the VIF value was 3.56 (This paper does not report this result due to space constraints. Information is available for readers upon request.).p value was zero for the Hausman test, indicating that the initial hypothesis should be rejected. A fixed effects model is recommended. rein are 0.032 and 0.012, respectively. When control variables such as industrial proportion, urbanization level, economic development level, and human capital level are included, the coefficient of the GEI is significantly positive at the 1% level. These findings show that increasing government environmental spending supports regional green development, thus confirming hypothesis 1. This conclusion is contrary to that of Ye and Guo [Panel regression was employed to analyze data from 30 Chinese provinces from 2005 to 2021 to determine whether the GEI could promote green development. GEI is the explanatory variable, and the level of green development is the explained variable. Two estimation techniques, random effects (Re) and fixed effects (Fe), were applied based on regression model (1). Each technique was estimated with and without control variables. The  and Guo , who use and Guo , thus heRegarding the control variables, the coefficient of urbanization was 0.012 and significantly positive at the 1% level. This finding implies that urbanization encourages green development. This might be because it promotes the rapid flow of production factors, such as labor and capital. It may also enhance the green transformation of local businesses and industries, while transforming old industries and enhancing and eradicating backward production capacity. In addition, the coefficient of industry proportion was significantly negative, indicating that it does not favor the advancement of green development. In other words, the more industrial added value there is in the local economy, the more local green development is hindered. Additionally, the coefficient of R&D investment was significantly positive at the 5% level, indicating that it promotes green development. Finally, although the coefficient of industrial percentage is positive, it does not pass the significance test, meaning that it does not favor the advancement of green development. This may be because China’s economy develops as energy consumption increases, and high energy-consuming and carbon-emitting activities expand, thus hindering green development in the short term.p value is 0. This makes use of a fixed effects model. The regression results are displayed in Column (1) of The GEI could offer financial assistance for green development in environmental pollution control, green enterprise transformation and the application of new energy, and resolve the issue of insufficient funding in the early stages of regional green development. Capital investment, however, has an inertial delay characteristic, which suggests that previous government environmental expenditures may theoretically affect current green development. To test the findings, this study incorporated the current period and lag period I and employed fixed and random effects models. According to the Hausman test results, the As shown in Referring to Zhang et al. , hypotheConsidering that missing variables and measurement errors cause endogeneity problems with inconsistent estimation results, instrumental variables were introduced to conduct a robustness test for potential endogeneity problems. Compared to estimation methods such as two-stage least squares, the biggest advantage of the generalized method of moments (GMM) is that it does not specifically restrict the overall distribution, focusing only on certain characteristics such as moment conditions, which offer the estimated value of a property of a large sample. Referring to Khan et al.  and Fan The panel regression results verified the first hypothesis. GEI plays a positive role in promoting green development; however, this effect is indirect. Therefore, the two approaches were investigated separately, based on the indirect impact mechanism of the GEI. pau and ene are 0.173 and 0.091, respectively, and at least pass the 5% significance test. This finding suggests that economic agglomeration and the application of green patents can help advance regional green development. Additionally, for the two action routes, the GEI coefficients were significantly positive at the 5% level, indicating the existence of partial mediation. This analysis implies that the GEI is conducive to regional green development through the application of green patents and economic agglomeration. Thus, the second and third hypotheses are verified.The estimation results are shown in columns (1) to (3), with the application of green patents acting as the mediator; and in columns (1), (4), and (5), with economic agglomeration acting as the mediator. The regression results from the first phase were validated in a previous study following the mediating effect verification approach. To determine whether the GEI had a meaningful effect on the two mediators, the second step was to test GEI plays various roles in green development with different effect intensities because economic development, the level of government investment, and tax policies differ from region to region. Therefore, this study further separated the data into eastern, central, and western regions. The regression results are presented in The GEI coefficients in the central and western regions fail to pass the significance test, with coefficients of 0.002 and -0.002, respectively, indicating that GEI has not significantly impacted the central and western regions yet. Simultaneously, according to the sign of the coefficient, the correlation between GEI and green development in the western region is negative. This conclusion is similar to the one drawn by Li and Bai  when anaIntegrated Reform Plan for Promoting Ecological Civilization was issued in September 2015 to implement this spirit. At the same year, the concept of green development was also officially stated. Both of them proposed higher requirements for protecting the ecological environment in various regions. Therefore, this study divided the data into three periods for analysis, 2005–2011, 2012–2014, and 2015–2021. The results are summarized in At different times, the national emphasis on the ecological environment and the environmental investment behavior of local governments varies greatly, causing temporal heterogeneity in green development. In 2012, the 18th National Congress of the Communist Party of China included the construction of ecological civilization in the overall layout of the “Five in One” for the first time, elevating it to a national strategy. Concepts like “clear waters and green mountains are as good as mountains of gold and silver” became more popular than before. Besides, the Reform Plan forced local governments to focus on regional environmental protection and invest in green transformation and development, thus playing a positive role in green development in the region.During 2005–2011 and 2012–2014, the coefficient of GEI decreased, and its sign changed from positive to negative, indicating that although the 18th National Congress of the Communist Party of China clearly proposed to vigorously promote the ecological civilization construction, local governments did not perform well in protecting ecological environment. Supported by the research of Wu and Qian, this finding might be explained by the shortage of regulations and proper management over such a short period . FortunaAs an environmental-friendly and sustainable way of development, green development exerts an important impact on achieving the strategic goal of “carbon peak and carbon neutrality” and promoting high-quality economic development. This study tested the mechanism of the GEI in green development using the fixed effects model and a mediating effect model based on panel data of 30 Chinese provinces from 2005 to 2021. Regions and times were included in the heterogeneity test. The results of this study are as follows: First, green development is encouraged by government environmental investment. The findings are robust even after switching the measurement. Note that the positive effect of the GEI decreases over time and cannot support green development further. Second, the transmission mechanism demonstrates that the environmental investments made by the government would enhance the environment through the application of green patents and economic agglomeration. Finally, according to the heterogeneity analysis, green development in eastern China was more obviously affected by the GEI, and such investment played a more significant role in promoting green development after 2015. The following recommendations are made based on the research findings:Scaling up input and improving the level of administration. First, provinces, cities, and counties must develop ecological and environmental protection initiatives according to their administrative divisions and geographical settings. It is preferable to encourage regional businesses to participate in project governance. Governments at all levels should allocate specific funds for environmental protection and offer financial support through grants and subsidies, project subsidies, and tax breaks to ease the financial burden on businesses caused by environmental management. More favorable policies and funding must be provided to areas with major pollution problems and remarkable control successes to reduce regional ecological and environmental pollution, point by point and level by level. In addition, establishing a sound green performance assessment mechanism, rating evaluation mechanism, and long-term environmental accountability mechanism and regularly verifying the status and purposes of environmental protection funds is prudent. Priority support should be given to policies and projects with high performance levels, in principle, while those with low efficiency and high repetition should be cancelled. Conducting in-service and resignation reviews of the performance of relevant ecological and environmental leaders is also vital. The leaders of projects with unclear statuses and insignificant benefits should be interviewed. Those who fail to perform by themselves should impose administrative penalties.Fully realizing the mediating effect of economic agglomeration and green patent application. Units at all levels must be urged to overcome administrative divisional barriers and share infrastructure like sewage treatment infrastructure to enable companies to congregate in one location. Companies with significant upstream and downstream correlations within an industry or considerable inter-industry contact should be placed compactly. Businesses in the cluster region should receive specific policy and financial support in domains such as the green project declaration, environmental tax exemptions and subsidies, and business finance. However, hastening the application for green patents and boost the effectiveness of low-carbon technological applications is preferable. First, scientific research institutes, colleges, universities, and businesses, should have access to platforms to innovate green technologies. Several units must encourage policy and financial support for patents, as well as the R&D of low-carbon technology. New-generation information technologies, such as blockchain and 5G, should be introduced to explore new application scenarios and provide convenience for specific applications of green technology. Second, patent owners and related companies should be urged to open innovative low-carbon patents to promote the broader adoption of green technology. The government should pay a patent transfer fee at least as high as the market rate or offer open business policy convenience to encourage the flow of patents into the market. Finally, the threshold for obtaining green invention patents should be raised. Patents with specific possibilities for future use should be produced and green patents that meet the criteria for timely application and ecological value should be awarded and supported.Improving the accuracy of government environmental investment by adopting measures for local conditions. Significant disparities exist in terms of geography, economic development, and financial soundness between China’s eastern, central, and western regions. Consequently, the effect of GEI on green growth also varies. For the Eastern region, which has a robust economy and many environmental-friendly businesses, fully utilizing its skills and industrial advantages is important. Additionally, it should take the lead in promoting low-carbon technologies and environmental management experiences to help solve environmental issues in the central and western regions. Besides, a sound policy for green talents concerning their living, education and family should be established to attract more enterprises and high-quality labor. Establishing strict environmental standards concerning aspects such as sewage treatment and carbon emissions for heavy-industry companies is also necessary. In addition, financial support from green banks and funds should be provided to assist these businesses in updating their production machinery and developing new production techniques. Finally, the government should encourage firms in eastern China to collaborate with other companies to form a support system.In summary, this article discussed the relationship and mechanism concerning the GEI and green development and empirically tested relevant hypotheses. This study established a preliminary theoretical framework for the relationship between the GEI and green development in China from the perspective of economic agglomeration and green patent application. Simultaneously, for regions with different levels of economic development and resource endowment in China, effective use of the positive impact of the GEI and promoting regional green development balance is of great significance. Microdata at the district and county level as well as enterprise data can help this study explore the relationship between GEI and green development more deeply. However, this study was not able to obtain relevant data because of its unavailability; therefore, further research is needed to explore the link between GEI and green development in more detail.S1 File(XLSX)Click here for additional data file.


Accurate prediction of vehicle acceleration has significant practical applications. Deep learning, as one of the methods for acceleration prediction, has shown promising applications in acceleration prediction. However, due to the influence of multiple factors on acceleration, a single data model may not be suitable for various driving scenarios. Therefore, this paper proposes a hybrid approach for vehicle acceleration prediction by combining clustering and deep learning techniques. Based on historical data of vehicle speed, acceleration, and distance to the preceding vehicle, the proposed method first clusters the acceleration patterns of vehicles. Subsequently, different prediction models and parameters are applied to each cluster, aiming to improve the prediction accuracy. By considering the unique characteristics of each cluster, the proposed method can effectively capture the diverse acceleration patterns. Experimental results demonstrate the superiority of the proposed approach in terms of prediction accuracy compared to benchmarks. This paper contributes to the advancement of sensor data processing and artificial intelligence techniques in the field of vehicle acceleration prediction. The proposed hybrid method has the potential to enhance the accuracy and reliability of acceleration prediction, enabling applications in various domains, such as autonomous driving, traffic management, and vehicle control. With the rapid development of sensor technology, how to use the data obtained by sensors is a crucial issue, and data prediction is an important part of it. Take Hybrid Electric Vehicles for example, the Energy Management Strategy determines power allocation among multiple energy sources and is crucial for lowering fuel consumption and emissions, in which a data-based model is often used ,2. The ICommonly employed prediction approaches are typically separated into model-based methods and data-based methods ,8. The MWithout fixed parameters or structures, data-based prediction systems usually depend on the characteristics of the data. For example, Ref.  developeGiven the variety of acceleration patterns, this study proposes a method for predicting acceleration that uses a combination of clustering and deep learning to improve accuracy.By clustering driving data into groups based on their features and then using different prediction networks on each group, the experiment showed that different prediction networks work well with driving data that have different features.The probability of each data segment belonging to each Gaussian mixed model cluster is calculated, and then the data are taken as a new input to the neural networks.Extensive experiments were carried out using a public database, and the performance of the proposed method was compared to that of unclustered data and existing works. This showed that the proposed method is better at improving the accuracy of predicting acceleration.To address the aforementioned issues and improve prediction accuracy, this paper proposes a hybrid acceleration prediction model based on clustering and deep learning. Since different prediction networks based on deep learning work best with different data features, we first clustered historical driving data to group together data with similar acceleration patterns. Then, different prediction networks are used for different clusters of data. Unlike existing work that partitions data based on drivers, our work clusters data based on the characteristics of vehicle acceleration data itself . Since tThe remainder of the paper is structured as follows: This paper proposes a hybrid method that combines clustering and deep learning to improve the accuracy of acceleration prediction. In this section, we will introduce the basic idea and general process of the method. The main task of the data preparation phase is to divide the long-term driving data into short acceleration process segments. Each raw record in the database has a long driving process that may include more than one acceleration. In order to facilitate the analysis of each short acceleration process and the subsequent deep learning, the original data are segmented based on some criteria so that the time length of each segment can basically contain a short acceleration process. The specific segmentation criteria will be detailed in a later section.The goal of the clustering phase is to classify the segmented data based on driving characteristics so that different classes have distinctly different feature values. Numerous factors affect each acceleration process . Not onlThere are numerous clustering algorithms with varying degrees of effectiveness, which can essentially be classified as supervised learning and unsupervised learning. Since supervised learning requires prior explicit knowledge of the category information of each classification and the premise that all items to be classified belong to a category, it is frequently impossible or prohibitively expensive to satisfy the aforementioned conditions when working with large amounts of data .When working with large datasets and without a clear classification criterion, unsupervised learning, namely clustering, is frequently utilized for classification . In thisThe last phase is to use deep learning to train and predict using each cluster’s acceleration data segments. Since each cluster of driving data has different features, the performance of different prediction networks for a given cluster may be very different. In this paper, we use two candidate networks, LSTM and GRU, to train and predict separately for each cluster’s data and select the one with better prediction performance for each cluster. To evaluate the prediction performance, the Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) are chosen as evaluation metrics.To verify the clustering effect, we directly applied LSTM and GRU to the entire dataset without clustering and compared the results with those after clustering with the proposed method. To verify that different prediction models are suitable for different clusters, we applied LSTM and GRU separately to each cluster and compared and analyzed the results. To fully evaluate the performance of our proposed method, we also compared the prediction accuracy of some typical algorithms.Details will be provided in the following sections.The dataset we used is based on the Next Generation Simulation (NGSIM) database . The datDue to the imperfection of the original dataset, which includes errors, redundant data, and missing data, the errors will be amplified when using neural networks to predict speed and acceleration, thus affecting the accuracy of the predictions. Therefore, before proceeding with further processing of the original dataset, a dataset-cleaning step is necessary. Redundant and erroneous data are removed, and missing frames with fewer than 10 occurrences are filled using corrected Akima piecewise cubic Hermite interpolation .N frames of data, and each frame includes the following information: data index i, vehicle velocity n sample segments, some criteria for segmenting data are demonstrated as follows:It should be noted that each frame represents 0.1 s because the data are sampled at a rate of 10 Hz. The preprocessed dataset consists of (1) To ensure that the data used for subsequent clustering and prediction includes complete acceleration or deceleration processes, data segments with a travel time of less than 60 s are excluded.(2) We consider that the influence of the preceding vehicle on driving behavior is not significant when the distance exceeds 80 m. Therefore, in the data records, distances exceeding 80 m from the preceding vehicle are treated as 80 m.Based on the aforementioned criteria, we set that each segment contains 200 frames of data. In subsequent processing, we treat each segment as a unit when clustering but extract features using frames in each segment in Although we attempt to estimate acceleration, it is insufficient to rely solely on acceleration data. Therefore, we first calculate the commonly used feature parameters of each data segment, i.e., average velocity n frames. Each frame contains the feature parameters extracted from the corresponding data segment.After feature extraction, a dataset consisting of feature parameters is formed, with a total of The features used include vehicle speed, acceleration, and distance to the preceding vehicle, with significant differences in the numerical values of different features. In order to ensure that each feature has a comparable impact on clustering, it is necessary to normalize the features. The normalization formula used is as follows:In the normalization process of the extracted features for vehicle acceleration, The dataset consisting of feature parameters is now transformed into a dataset consisting of the normalized components, denoted as There are various clustering methods available for data analysis. GMM is not only more flexible in splitting the shape of clusters but also takes into consideration the data variance and is unbiased . ConsideBecause Gaussian Mixture Clustering requires solving for the maximum likelihood of the likelihood function, it can be implemented using the Expectation-Maximization (EM) algorithm.k, the mean First, we set the parameters of the Gaussian Mixture Model, including the number of mixtures n data points and k clusters, the probability that the i-th data point j-th cluster is:j-th Gaussian mixture component or cluster, i-th data point, j-th mixture component, and i-th data point on the Gaussian distribution given the j-th mixture component, which is:Then, the Expectation (E) Step: calculate the probability of each data point belonging to each mixture and calculate the posterior probability of each data point belonging to each cluster using Bayes’ theorem. As we have j-th cluster, we have:Then, the Maximization (M) Step: Based on the posterior probability obtained in the E step, update the mean, covariance matrix, and mixing weight of each cluster. Specifically, for the parameters of the E and M steps to iteratively update until convergence, which was determined by the change in the model likelihood function.Utilize the To address the issue of correlation between features, regularization is used to help mitigate multicollinearity and thereby promote the selection of independent and informative features . The regk, selecting the appropriate number of clusters is crucial for effectively classifying vehicle driving situations and predicting vehicle acceleration in subsequent steps. Therefore, the model selection tools, Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) are used to assist in determining the optimal number of clusters [Although GMM is not particularly sensitive to the choice of the number of clusters clusters . They arn is the number of driving data segments and h is the number of parameters, which means the principal components, i.e., 2 in this study. j-th Gaussian distribution.The number of estimated parameters is the AIC, while the BIC indicates the quantity of observations and calculated parameters. They are defined as follows:When comparing multiple models, the one with a lower value of AIC and BIC is better. The measured AIC and BIC of these five clusters for our dataset are shown in It can be observed that the AIC and BIC values tend to decrease before the number of clusters approaches 5, and that their values increase when the number of clusters exceeds 5. Therefore, the number of clusters is fixed to 5. The corresponding velocity and acceleration image after clustering is shown in k-th Gaussian distribution is computed using. Finally, as each cluster’s mean G represents the Gaussian distribution. The Gaussian distribution with the highest weight for In real applications, data can be collected online through a shared system. When each vehicle collects a sufficient amount of data, the vehicles can use their own driving data to build models as well. At first, the real-time data are first divided into segments of 20 s each. Missing frames in segments with few missing values are filled using corrected Akima piecewise cubic Hermite interpolation. Additionally, data points with a distance greater than 80 m to the preceding vehicle are replaced with 80 m. Then, the feature values are computed for each data segment, and all the features are normalized using the same method mentioned before. Since the weights This section will describe how to anticipate acceleration for clustered data based on machine learning. Due to the continuous nature of driving, the deep learning network model based on vehicle data for this project must be capable of fitting time series data appropriately and effectively. Since each cluster of data has its unique features, no network can perform optimally on all clusters in general. Thus, the methodology of evaluating the prediction performance of various neural networks on distinct clustering data is adopted, and then the most suitable model is selected for each cluster based on its performance. In this study, the performance of the four networks in the clustering-prediction hybrid model is compared, and the comparison procedure is detailed below.Under the framework of acceleration prediction tasks, not all networks can achieve satisfactory performance. Although one-dimensional CNN networks have been used in some papers for acceleration prediction, CNN networks are not fully suitable for the sequence here. CNN is primarily designed for image processing tasks, focusing on capturing spatial relationships. In the case of vehicle acceleration prediction, which involves time series data, CNN cannot directly model sequences, as it lacks explicit mechanisms to handle temporal dependencies and sequence evolution . CNN’s pIn contrast, LSTM and GRU networks offer distinct advantages in vehicle acceleration prediction. Due to their gated mechanisms, LSTMs and GRUs can effectively capture long-term dependencies in driving sequences, which is crucial for predicting acceleration patterns that may span across various time intervals. Their memory cells and gating mechanisms allow them to retain relevant contextual information and process sequential data more effectively than traditional RNNs. By using an LSTM-GRU hybrid network, we can potentially leverage the strengths of both architectures, benefiting from the memory and context preservation of LSTMs while enjoying the simplified structure of GRUs . This hyIn the previous section, we compared the differences between various networks and explained the reasons for choosing the LSTM and GRU networks over other networks. In the following, we will provide a detailed description of the specific structure of the LSTM and GRU networks.We define the driving process as the process of modifying the vehicle indicators over time. In this experiment, we use pre-clustering and post-clustering data with varying lengths of driving data segments as input to the hybrid network to train and compare the performance of several acceleration prediction networks. Experiments demonstrate that the accuracy of predicting acceleration is greatly enhanced after clustering compared to before clustering.Recall that we divide the corresponding sample data segments into multiple classes based on the clustering results. Each frame of data, represented as In this experiment, each data segment represents the motion process of a car within 20 s. Therefore, we used a hybrid prediction network to extract and shape features from the first 150 frames of the data segment to learn the car motion process. Then, the network generates vectors representing predicted future acceleration values based on the motion process. In order to verify the performance superiority of this network in short-term and long-term prediction, we used data from the last 20 frames and 50 frames of the data segment as an evaluation of actual results, representing the performance of the network in two scenarios: short-term prediction (2 s) and long-term prediction (5 s), respectively.Since clustered data with different data characteristics may perform differently under various networks, we compare the performance of the LSTM and GRU networks for each clustered data type to choose the network that performs better. By utilizing LSTM and GRU networks in a hybrid architecture, one can leverage their strengths in handling long-term dependencies and preserving contextual information, thereby improving the accuracy and performance of vehicle acceleration prediction models. Hence, we select the LSTM-GRU hybrid network as the primary network for this experiment to achieve better performance. The following sections will provide a detailed review of the LSTM network and the GRU network.Instead of focusing on the performance of the network models themselves, the objective of this study is to compare the performance of prediction models based on pre-clustering and post-clustering datasets. Therefore, LSTM networks and fully connected layers are introduced in the clustering-prediction mixed model as a deep learning model for acceleration prediction.The LSTM network, a special variant of RNN network, is often used to process sequence data and solve long-term dependency problems. It has been used in many experiments for acceleration prediction and has shown excellent performance. The core idea of LSTM is to introduce memory cell and gate structures to better capture and process long-term dependencies in sequences. Compared to traditional RNN, LSTM has strong memory ability and can better preserve and utilize past information.The LSTM network framework used for acceleration prediction, also known as gate structures , mainly In practice, the overall trend of acceleration prediction network performance shows that as the number of training epochs and neurons increases, the speed of network performance optimization slows down and even declines. The reason is that the data feature dimension used for acceleration prediction is smaller, and when the number of neurons is much higher than the feature dimension and the number of training epochs is too large, the network is easy to overfit, resulting in a poor acceleration prediction effect. The parameters used to construct the LSTM network are displayed in The experimental results show that only introducing the LSTM network into the clustering-prediction mixed model for acceleration prediction has great limitations. The performance of an acceleration prediction model needs to be judged not only by the prediction accuracy of the model, but also by the time required for training the model and making predictions. Shorter time requirements usually mean better adaptability, where the LSTM network tends to fit slowly in tests predicting acceleration. Therefore, the GRU network, which in general takes less time than an LSTM network, is also selected as a candidate prediction network for the clustering-prediction mixed model.The GRU network is a uniquely enhanced and accelerated LSTM network. Similar to LSTM, GRU is also a chain model composed of repeated gated structural unit modules. However, GRU combines the forget gate and input gate of the LSTM gate structure, enabling the network to have fewer parameters. Therefore, compared to the LSTM network, GRU has unique improvements in handling driving data. The improvement of the network is reflected in the shorter fitting time of the GRU network for subsequent acceleration prediction on driving data, providing it with a distinct advantage when dealing with large batches of training data .The addition of the GRU network provides a novel contrast to this experiment, as the GRU network tends to have a faster fit in tests predicting acceleration using neural networks trained on historical traffic data. In trials utilizing a neural network trained on historical traffic data, the GRU network tends to fit faster. It produces diverse outcomes for various data clusters and LSTM, and it performs better when clustering data with a specified distribution. For clustering data with different distributions, one of the LSTM network or the GRU network can be selected to achieve a more optimized acceleration prediction performance, which enables the clustering-prediction mixed model to have better versatility.The platform and related parameters used to build the GRU network are the same as those used to build the LSTM network. We train the GRU network using the same fixed-length data segments of 150 frames as with the LSTM network, so as to control variables and realize the comparison between different models. The network structure is shown in The first step in prediction is to select an appropriate input data length for the neural network as well as the time length of the data to be predicted. Too long or too short time lengths can have an impact on the network’s prediction performance and practical value. As a result, taking into account the demand for prediction duration in practical application scenarios, we use 10 s of historical data to predict 2-s and 5-s accelerations, respectively, to evaluate the network model’s performance in predicting short and longer periods.The dataset used in this experiment consists of a total of 37,526 acceleration segments, which are obtained based on the NGSIM database. We processed the dataset by randomly dividing 70% of it as the training set and the remaining 30% as the test set. For each cluster’s data, different networks are trained and tested independently.Driver intentions and behaviors can differ greatly depending on the driving environment and road conditions, so the data characteristics of different clusters may also differ. Deep learning neural networks built on different clusters of data will produce different prediction results. In order to explain and compare the quality of prediction results, RMSE and MAE are introduced as evaluation metrics, which are defined as follows:To verify the clustering effect and find the more suitable network for each cluster, we used LSTM and GRU to make predictions on different data. Firstly, we directly used LSTM and GRU networks to make predictions on the entire dataset that has not been clustered, and then we used LSTM and GRU to make predictions separately on the data of the four clusters we divided. The experimental results for the 2-s prediction and 5-s prediction are shown in From the data in these two tables, it can be observed that, regardless of whether the same LSTM or GRU is used for acceleration prediction, half of the results are significantly improved after clustering. Furthermore, the average result, which takes the sample size into account, is also improved. This demonstrates that clustering data with similar driving characteristics before training prediction models can improve prediction accuracy.In addition, by comparing the prediction results of LSTM and GRU on each cluster, it can be seen that the two networks have different performances. However, no network outperforms the other on all data clusters, which also underscores the importance of selecting the appropriate prediction network for each cluster. Based on the two tables above, we can select the most suitable prediction networks for each cluster according to their performance, which has been highlighted in red in the tables. LSTM networks based on Cluster 2, 3, 4, and 5 clustering data perform better. The performance of GRU networks based on Cluster 1 clustering data is superior.Based on the highlighted data, the prediction results for Cluster 1, 2, 4, and 5 are superior to those of unclustered data. Specifically, It is worth noting that the results for Cluster 3 are even worse when compared to those of unclustered data. By looking at the data in Cluster 3, we found that the acceleration changes a lot and that Cluster 3 has the highest variation in acceleration. This makes it hard for LSTM and GRU to make good predictions with this type of data. Although these data also exist in the unclustered data and lead to poor prediction results, the better results of other clusters of data will average them out, making the results better than those of Cluster 3.Park and Ahn  used an Ref.  proposedIn this paper, a clustering-prediction model for predicting vehicle acceleration is proposed. The main innovation is to divide the acceleration processes into different clusters with different acceleration patterns, and then choose different deep learning prediction networks for each cluster, thereby improving the adaptability and fitting of both the prediction network itself and the hyperparameters, and achieving the goal of improving acceleration prediction accuracy. To distinguish between different acceleration patterns, GMM is used to cluster the vehicle driving data segment. GMM is used to cluster the vehicle driving data segment in order to distinguish between different acceleration patterns. During clustering, regularization is used to prevent the model from overfitting. Each cluster in the clustered data has its own acceleration characteristics, and therefore, may require a different prediction network. LSTM and GRU networks are tested for the data in each cluster, and the better-performing network is then selected for each cluster. The experimental results demonstrate that the proposed method performs well in terms of improving acceleration prediction accuracy.The method proposed in this paper can be used in vehicle fuel control, where fuel allocation is made more rational by predicting the driver’s behavior in order to reduce energy consumption and keep the vehicle running longer. It can also be used in vehicle safety systems, where the driver’s behavior is predicted in order to make advanced predictions and countermeasures for potential hazards.