Governing the Nexus of Water, Energy and Food: The Case of Wastewater Reuse in Agriculture
Synergies are required to ensure coordination between UN agencies (on norms and indicators), Member States (on coherence of policy instruments) and consumers (on perceptions of safety and affordability of services) to advance the achievement of Sustainable Development Goal (SDG) target 6.3 which focusses on reuse of wastewater. In this paper we employ theoretical insights derived from an agent-based modeling approach to undertake a critical examination of the recent UN-WATER directive on SDG target 6.3 and advocate for an improved understanding of factors that determine whether and how effective wastewater reuse will be possible while accommodating for regional variation and institutional change.
We demonstrate that by applying the Nexus approach it is feasible to overcome siloes by forging concepts of trade-offs and synergies to draw out coupled perspectives of bio-physical and institutional dimensions of water-energy-food interactions. By employing this proposition, the paper advocates for place-based observatories as a mechanism that can support valorization of data and methodological assumptions as a precursor to robust monitoring of the SDG’s. The systematic use of literature reviews and expert opinion to develop and pilot-test composite indices via place-based observatories raises the prospect of a data light approach to monitoring SDGs; specifically, what are the merits of relying on extensive survey data compared to composite indices that while being amenable to supporting benchmarking and scenario analysis can provide the insight needed to inform decision-making and robust monitoring of global goals?
Monitoring Sustainable Development Goal (SDG) Target 6.3 on Wastewater Reuse: Method, Data and Applications of Agent Based Modeling
The gulf between theory and practice in Global Public Goods Research1 has become apparent in recent years. For instance, International organizations such as the Consultative Group on International Agriculture Research (CGIAR) have for their part placed a premium on adoptionrates for technical options that encourage resource recovery and reuse as an indicator of the effectiveness of international development assistance. However, a recent CGIAR Standing Panel on Impact Assessment synthesis report found adoption rates for full-fledged NRM technologies2 to be remarkable and consistently low, ranging between 1 and 10% in areas where a variety of actors had been promoting these technologies (Stevenson and Vlek, 2018).
Similarly, research on the merits of integrated billing for water supply and sanitation in the Netherlands showed that consumers stood to benefit in terms of less time and money spent on administration (Salome, 2010). However, despite the efficiency gains that could arise from overcoming administrative siloes combined billing has not succeeded because this would require the Water Boards (responsible for sanitation) and private companies (responsible for water supply) to give up some of their autonomy with regards to their sources of financing (see also Howarth and Monasterolo, 2016; Yang et al., 2016; Weitz et al., 2017).
These examples outlined above highlight a key issue that speaks to the question posed by this Special Issue: Achieving Water-Energy-Food Nexus Sustainability- a Science and Data Need or a Need for Integrated Public Policy?: there is a lack of understanding of the institutional pathways (mediated by state and market mechanisms) for adoption of the results of controlled experiments and case studies.
Recognizing the lack of understanding of (i) the institutional environment (i.e., property rights, legal and policy framework), (ii) the trade-offs involved in decision making and (iii) administrative culture and policy priorities, an agent-based modeling approach has emerged to emphasize the use of role games and experiments to collect data as well as having stakeholders involved in validation of multi-dimensional models (Barreteau et al., 2010; Poteete et al., 2010, p. 13). Agent-based modeling can potentially support analysis of the Sustainable Development Goals (SDGs) because it emphasizes the need to examine mechanisms for coordination and information sharing among networks of public agents, in the absence of which synergies in decision making fail to emerge.
Specifically, with reference to SDG target 6.33 synergies are required to ensure coordination between UN agencies (on norms and indicators), Member States (on coherence of policy instruments) and consumers (on perceptions of safety and affordability of services) to ensure effective reuse of wastewater. The failure to ensure coordinated action could exacerbate unintended consequences of policy action.
Political Economy of Public Decision Making in the Water-Energy-Food Nexus
In existing literature on public choice and New Institutional Economics (NIE), we can find some theoretical propositions that promote understanding of synergies in environmental planning and management. For instance, rational choice scholars imply that improved information could potentially overcome the effect of siloes through coordinated and evidence-based decision making (North, 1990; Ostrom, 1990). NIE scholarship, on the other hand focuses on the aspect of strategic interaction4 in the decision- making process. This scholarship implies that decisions of officials within public agencies need not be made merely based on available information (i.e., data and evidence) but more on strategic considerations (Eggertsson, 1990; Harriss et al., 1995).
The analysis of the role of data and evidence in decision-making process would be enhanced by acknowledging historical specificities of the institutional environment. This is precisely because these historical specificities shape subsequent choices in environmental planning and management i.e., whether to prioritize infrastructure construction or service delivery, promote centralized or decentralized governance, and emphasize public or private service delivery models (Pollitt and Bouckeart, 2000; Abelson, 2003).
It is pertinent to acknowledge in this context that the trajectory of Global Public Goods Research on Natural Resource Management (NRM) has itself undergone a shift in emphasis toward understanding the role of institutions in environmental planning and management. In the tradition of the “stages of growth” model of economic development, scholarship has iteratively emphasized the role of extension agencies such as forestry and irrigation departments in: (i) establishing infrastructure, (ii) enabling well-functioning markets for distribution of seeds and fertilizers, and (iii) disseminating information on management practices on the assumption that these interventions will boost agricultural yields and with the expectation of a positive effect of their adoption on levels of poverty and hunger (Brohman, 1996; Dorward et al., 2005; Shiva, 2010; Food Agriculture Organization, 2014).
Recent Nexus scholarship has begun to emphasize the importance of agent-based modeling to systematically analyse the role of social networks, institutional capacity and information sharing within and between departments responsible for management of water, energy and food (Harwood, 2018; Portney et al., 2018; Uden et al., 2018). However, formal models often work with unrealistic assumptions and without addressing the gap between theory and practice and thus do not explain the behavior of public agencies and agents in a comprehensive manner (Poteete et al., 2010, p. 4; Smajgl and Ward, 2013a).
Against this background, it is feasible to overcome siloes by forging concepts of trade-offs and synergies to draw out a coupled perspective of bio-physical and institutional dimensions of water-energy-food interactions. By employing this proposition, the paper advocates for place-based observatories as a mechanism that can support valorization of data and methodological assumptions as a precursor to robust monitoring of the SDG’s.
Monitoring Sustainable Development Goal (SDG) Target 6.3 on Wastewater Reuse: Method, Data and Applications of Agent Based Modeling
Conventional unidimensional approaches emphasize: (1) a disproportionate focus on analysis of behavior of bio-physical resources; (2) efficiency of ecological systems; (3) statistical analysis of interactions between SDG goals and targets; and (4) case study research-data, models and approaches that have neither been pilot-tested nor valorized through engagement with governance structures and processes (see Cai et al., 2017; Bleischwitz et al., 2018; Dombrowsky and Hesengerth, 2018; Liu et al., 2018; Scott et al., 2018), and thus could promote siloes in environmental planning and management with potential to seriously undermine the credibility of the global monitoring regime.
Our proposed approach, on the other hand, advocates for improved understanding of the factors which determine whether and how effective wastewater reuse is possible while accommodating for regional variation and institutional change. As demonstrated in this paper, the proposed Wastewater Reuse Effectiveness Index (WREI) composed of both bio-physical and institutional components, relied upon data valorization, expert opinion and coupling of bio-physical and institutional perspectives of water-energy-food interactions with potential to effectively monitor SDG 6.3. Further, WREI showcases cutting edge applications of the Nexus approach6 in managing trade-offs and fostering synergies in environmental planning and management (Kurian and Ardakanian, 2015; Scott et al., 2015).
Monitoring Sustainable Development Goal (SDG) Target 6.3 on Wastewater Reuse: Method, Data and Applications of Agent Based Modeling
Agriculture has today become a key driver for four of the eight Planetary Boundaries (PB’s) (identified by Rockstrom et al., 2009) that are at a critical stage of risk: freshwater use, biogeochemical flows, changes in biosphere integrity and climate change (Campbell et al., 2017). We could deduce from the arguments of “stages of growth” theorists that as economies grow infrastructure begins to play an important role in connecting populations to services in the form of irrigation, wastewater treatment or hydro-power. This is where planetary scale analysis of climate change, biogeochemical flows, biosphere integrity and land-system change need not necessarily align with decision making at administrative scale: plot, farm, local government or river basin authority.
In other words, while results of planetary scale analysis may emphasize the finiteness of water, soil and waste resources and advocate for recharge of aquifers, restoration of soils, multiple uses of forest ecosystems, extended life-cycle management of infrastructure or tax rebates for adoption of renewable energy, administrative scale decisions need not necessarily support policies, projects or programs that emphasize circular economy pathways such as reuse, re-manufacture, replace, reduce and retrofit (Destouni et al., 2013; Jaramillo and Destouni, 2015).
On the contrary political economy compulsions may drive decision makers to commit more resources toward exploitation of newer sources of water and energy without ensuring that established infrastructure is properly functioning. This may satisfy entrenched political interests but may exacerbate pressure on environmental resources (Agrawal, 2005). Given the stark divergence between planetary and administrative scales of analysis, five contemporary trends within the agriculture sector necessitate particular attention to enable a transition from a narrow focus on crop systems toward food systems:
(a) De-coupling of GDP growth from labor force participation in agriculture (Campbell et al., 2017), (b) increasing diversion of water from agriculture toward urban water supply reflecting a growth in secondary towns at the peri-urban interface, (c) changes in diets away from staples toward processed food reflecting changes in composition of labor force and changes in income and non-farm employment (Annexure 1), (d) Land sub-division with potential to affect the viability of farming operations especially in high-density tropics (Saith, 1992) and (e) the growing influence of transnational corporations for seeds, capital, pesticides, marketing and mechanization that has had the effect of exacerbating the separation of power from local politics and decision- making structures (Kurian, 2010).
Looking ahead to prospects for 2050 Hazell (2017) foresees growing differentiation within agricultural sectors in developing countries, with small farms becoming smaller and more numerous; more part time farmers, particularly among smallholders, for whom agriculture is a modest and diminishing share of household income and growing bifurcation between….young and elderly farmers and geographically well-situated regions (urban and peri-urban) vs. isolated, marginal rural areas. He therefore argues that agricultural research that take consideration of contemporary conditions with the goal of advancing poverty reduction, must consider a typology of different smallholder types with different resources, connections to markets and hence economic prospects and agriculture for development needs.
To these categories he adds, we must also add important differences in household structure and intra-household differences across farms, even within the same communities, and the culturally mediated roles of gender in access to land, irrigation water, forests affecting labor market participation and wages, which may systematically disadvantage women and girls and make them more directly experience poverty (Agarwal, 2001).
When integrative analysis of interventions is weak, we fail to account for rebound effects in development practice (Annexure 2). For example, a recent CGIAR assessment found that high levels of fertilizer subsidies (energy) in Zambia adversely affected rates of adoption of Integrated Soil Fertility Management (ISFM) (Stevenson and Vlek, 2018). This is where trade-off analysis can prove to be important in untangling the individual elements of the ISFM technology package into costs and negative externalities that are involved covering water, energy and food.
The subsidies on fertilizer make their application more likely than in other countries, but farmers stop after applying fertilizer and don’t do the other things that will build up soil fertility in the long-run. These reasons could be prohibitive effective labor costs of applying the other component practices; farmers not perceiving a benefit from the package as a whole; farmers not caring about long-term fertility (high discount rate) or that it is just not on their radar (short planning horizon).
Trade-off analysis may reveal the priorities and accompanying logic guiding decision makers within a given administrative jurisdiction as to which set of actions to prioritize. For example, who are the beneficiaries of energy subsidies and how does this compare with the interests of farmers with potential to benefit from adoption of ISFM? Further, are the equity concerns relating to increased women’s workload under irrigated agriculture likely to override the interests of those benefitting from expanded urban water supply because of catchment protection interventions? Therefore, trade-off analysis can inform targeting of development interventions in line with locally defined norms of fairness.
In situations where equity is prioritized for example, targeting may lead to design of subsidy schemes that focus attention on reducing income poverty among poorer households and increased investment of savings to improve productivity of livestock and agricultural assets (Standing, 2017). Cash Conditional Transfers (CCT’s), for example in Sri Lanka’s Samruddhi scheme resulted in improved child nutrition, while in other cases transfers that have increased productivity of agriculture and livestock have resulted in reduction in casual wage labor which tend to be lower paying among non-farm jobs.
Agent-based modeling emphasizes the importance of coordinated action to overcome siloes in decision making. Agent-based modeling of trade-offs will reflect the fact that policy and management choices that operate at global, national and local scales are guided by norms and agency and individual behavior that are focused on ensuring a balance between planetary scale imperatives of resource conservation/reuse and institutional priorities of effectively delivering critical public services at the appropriate administrative scale7 (Thaler, 2015). The degree to which institutional synergies are forged will determine the success with ensuring a balance and mitigating rebound effects in environmental planning and management.
When planning over-emphasizes either bio-physical or administrative imperatives rebound effects are bound to be amplified either in the form of environmental risks or institutional siloes. The level of divergence from the ideal, balanced scenario is depicted as the space between the blue continuous line and the blue broken line in Figure 1.
Figure 1. Managing rebound effects in the water-energy-food nexus. Adapted from Kurian and Ardakanian (2015), Kurian (2017), and Kurian (2018).
Historical institutionalist literature enables us to identify three components of robust synergies: (a) social networks that support information flows and knowledge exchange among different functionaries within and across departments, ministries and agencies, (b) deployment of complimentary skill sets (capacity) by key players and (c) a critical mass of financing and technology that can be appropriated by agencies and departments focused on achieving a particular policy goal (Gregory, 1997; Batley, 2004).
There are also several enabling factors for robust synergies, notably: (a) a clearly articulated legal and policy framework, (b) clear set of policy instruments for implementation of legal and policy framework that includes directives, guidelines, circulars, standards and notifications stipulating how choices regarding technology and financing options may be arrived at, (c) data and evidence on distribution of bio-physical and institutional risks, (d) manageable levels of administrative discretion with regards to interpreting and implementing policy instruments and (e) incentive structure (penalties and rewards) for compliance with policy instruments (Pollitt and Bouckeart, 2000; World Bank, 2009; Kurian et al., 2018).
In terms of a parsimonious model, co-provision offers insights on how one may examine the effect of synergies in environmental planning and management. The following are some elements of a co-provision model that
