Planning for low-carbon communities in US cities: a participatory process model between academic institutions, local governments and communities in Colorado

Abstract

Participatory process models combine the use of technical data with community participation to develop a sustainability plan relevant to each city. In this article, two case study applications in Denver, CO, USA and Broomfield, CO, USA use a participatory process, which combines teams from academia, local governments and community members to create city climate action plans. The participatory process is developed from concepts in community-based participatory research, analytic deliberation, and post-normal science. The refined process model developed in these two case studies goes through seven steps which include creating the deliberative body, co-developing data sets for sustainability analysis, defining sustainability goals, using scenario modeling for potential sustainability actions, prioritizing actions through deliberation, demonstrating consensus or diversity in final action plan, and conducting an outcomes assessment.

Figure 1.  ICLEI-Local Governments for Sustainability-USA five milestone process.

Adapted with permission from ICLEI-Local Governments for Sustainability [102].

Figure 2.  Valuing people, prosperity and the planet in the sustainability context by the task force and the community at large.

(A) Importance of various sustainability features focused on people, prosperity and planet, queried using practical examples; (B) Conceptual allocation of 30 total points between people, prosperity and planet.

Figure 3.  Benchmark energy/material use in buildings, transport and materials sectors for Broomfield and Denver.

Data quality is color coded as green (good local scale measured data), yellow (estimated local numbers) and red (state or national averages).

BTU: British thermal units; CDPHE: Colorado Department of Public Health and Environment.

Figure 4.  City of Broomfield task force decision-making criteria for climate action plans.

Respondents were asked to rate whether they considered a selection of sustainability programs important on a seven-point scale from strongly disagree to strongly agree.

Figure 5.  Illustration of sustainability wedges towards carbon reduction in communities.

Demonstrating the range of actions that can be taken in various infrastructure sectors (hypothetical example presented here for illustration only).

Figure 6.  Survey responses on policy opinions in Broomfield task force before and after analytic deliberation.

Cities are increasingly recognized as major sources of global GHG emissions [1,2] and, simultaneously, the primary action arena wherein sustainable energy development, creation of green jobs, and GHG mitigation and adaptation to climate change [3] will take place. In the USA, more than 900 US cities are participating in the US Conference of Mayors’ Climate Protection Agreement [101], pledging significant reductions in their GHG emissions. NGOs such as ICLEI - Local Governments for Sustainability USA are providing significant organizational and institutional support to local governments, including articulating a five-milestone process for sustainability planning in local communities (Figure 1)[102]. However, while these milestones are very useful, local governments still lack the resources – knowledge, monetary and personnel resources – to gather the requisite data for developing city-wide energy analysis, computing baseline GHG emissions, modeling alternative future energy scenarios and engaging community members and advisory bodies in prioritizing and implementing low-carbon infrastructures for the future.

An analysis of ten US cities that pioneered climate action planning in the previous decade (e.g., Portland, Oregon, USA; Seattle, Washington, USA; Berkeley, California, USA) identified several important knowledge gaps [4]. Cities needed more consistent protocols to measure their GHG emissions, improved metrics for benchmarking of energy-use data, and tended to rely overly on state and federal government programs to implement sustainable energy and low-carbon programs, codes and regulations. These gaps reveal important needs that can be filled by increased involvement of academic institutions in partnership with communities, local governments and non-governmental agencies, to assist communities in reaching sustainability goals; for example, academic research [5–8] is already informing the development of improved protocols for community-wide GHG accounting and energy use benchmarking [9].

For developing local community-specific actions that avoid over-reliance on federal laws, closer engagement between academia, the community and its leaders is needed, with the common goal of achieving sustainable development. Successful partnership models integrating academic expertise with local community-scale planning have been described in the peer-reviewed scientific literature in two main application areas pertaining to sustainability:

	▪ Environmental planning using the process of analytic-deliberation (AD) [10];
	▪ Public health outreach using community-based participatory research (CBPR) [11].

In this article, we briefly review these two approaches, and place them in the context of the broader social science research on cities, sustainability and learning. We then describe how key principles from AD and CBPR informed a participatory process model used by academia and local Colorado (USA) communities in planning and prioritizing sustainable energy and low-carbon policies in cities. The evolution of this participatory process model is described as it was adapted and revised with case study applications in two cities, beginning with Denver (CO, USA) in 2005–2007 and Broomfield (CO, USA) from 2009–2011, This continuous refinement has become an important feature of the participatory model used in the University of Colorado Denver’s (UCD) ‘Sustainable Infrastructure – Sustainable Communities Outreach Program’, which currently involves 20 communities ranging in size from those as small as 600 residents in a small mountain resort town (Central City, Colorado, USA) to a community of 50,000 people in the foothills (Broomfield, CO, USA), to the large metropolitan city of Denver (CO, USA) (600,000 people). The entire Denver Region (i.e., cities and towns around the Denver Metropolitan area such as Lakewood, Golden, Aurora, Broomfield and Westminster) encompasses aproximately 1.5 million people. If the processes and outcomes can be disseminated to the 80% of the population living in US cities, a 10% reduction in per capita emissions over 5 years (which is Denver’s goal), can yield a 2% reduction in global GHG emissions (given that the US contributes ∼25% of global emissions). Thus, the participatory process focused on low carbon planning has potential to have significant global impacts.

Each community is uniquely defined by its local culture, varying political leanings and differing sustainability priorities. Developing a common participatory process, and associated outcomes measurement tools, suited to such diverse communities, is the focus of this article.

Social science theories, AD & CBPR

▪ Background

Involving local governments and communities in low-carbon sustainability planning in a coordinated manner, in the absence of federal regulations, is unprecedented, and reflects a new type of multiscale governance [12]. Several researchers have described this multiscale governance as one that includes vertical hierarchy (nestedness with state and national governance), as well as horizontal connections and networks between local governments facilitated by NGOs such as ICLEI-USA, C-40 group and so on. [12–14]. Holgate further describes some of the actors involved in such governance and identifies types of knowledge required for embarking on climate action plans (CAPs); for example, technical knowledge about GHG emission footprints and institutional knowledge to implement actions, along with policy and issues champions [15]. Holgate also presents an interesting case study showing that access to information about GHG footprints of cities facilitated climate action planning in Cape Town (South Africa) by contrast, the lack of technical knowledge and resources inhibited the process in Johannesburg (South Africa). Access to technical information is thus seen as an important first step in developing sustainability plans.

How technical and scientific information plays a role in decision-making has been a topic of long-term research in the social sciences. ‘Normal science’ as defined by Kuhn [16], conceptualizes scientific inquiry as an objective process of ‘puzzle solving’ based on an accepted paradigm of thought about a particular subject matter. This traditional scientific approach views systems as something that can be taken apart, examined, and reassembled through controlled experimentation, statistical analysis and modeling. Furthermore, this view holds scientists as the experts that practice science while citizens are the recipients [17].

However, many scholars have become critical of the traditional scientific approach for its inability to deal effectively with risk management, particularly in the areas of environmental problems and health [18–22], citing an inability to deal with the complex social, ecological and economic aspects of interdisciplinary issues and the lack of multi stakeholder inclusion in decision-making processes.

In response, Funtowicz and Ravetz developed the concept of ‘post-normal science’ (PNS) during the mid-1980s [23]. This theoretical orientation, directly opposed the traditions of ‘normal science’, merges scientific knowledge with policy governance. Policy-relevant science, the authors argue, requires that ‘facts’ and ‘values’ are both considered in developing solutions to complex health and environmental problems [23]. PNS has achieved rigorous practice in some areas of medical research, such as AIDS research, where there are high personal stakes, political influences and uncertainty [24]. By definition, PNS is the most effective problem-solving method in cases where, “facts are uncertain, values in dispute, stakes high and decisions urgent” [25].

While Funtowicz and Ravetz address the application of PNS to medical research (e.g., AIDS research) [23], Paul Stern articulates the process of analytical deliberation that brings technical analysis to intersect with societal decision-making at large (e.g., in communities impacted by environmental pollution) [26].

Analytic deliberation refers to the process of scientific analysis combined with the democratic deliberative process. Similar to PNS, the method is grounded upon three guiding principles [26]:

	▪ “Scientific analysis by itself is inadequate for generating the understanding needed to inform policy decisions”, when the policy decisions involve the following elements: multidimensional and inequitable impacts, scientific uncertainty, differing values and mistrust (of scientists, data and of decision-makers), while there is simultaneously an urgency to act;
	▪ In such cases, subjective judgment is needed to develop collective understanding necessary for policy decisions, that can be achieved via the deliberative process wherein “…people discuss, ponder, exchange observations and views, reflect upon information and judgments concerning matters of mutual interest, and attempt to persuade each other”[18];
	▪ This combination of analysis and deliberation, termed AD, is believed to enhance collective understanding needed for environmental governance.

Analytic deliberation can direct scientific activity in order to inform practical understanding, “…allowing for those who are affected by science-based decisions to participate in framing the problems for scientific analysis, setting agendas, guiding the conduct of analysis when assumptions are in dispute, and interpreting the results”[26]. Good practice of AD requires at least the following:

	▪ Broad-based participation among stakeholders;
	▪ Utilization of good science with clear representation of uncertainty;
	▪ Explicit delineation of values and their role in interpreting data/model results and in decision-making;
	▪ Transparency in the rules of deliberation and rules for closure of the deliberative process demonstrating consensus or diversity of opinions;
	▪ Establishing criteria for reconsideration as new data become available.

Experimentation with the practice of AD with careful outcomes assessment is recommended [26]. AD has been used effectively in numerous practical examples of environmental risk assessment and mitigation in communities [26], in land-use planning incorporating multi-objectives decision-making [10], and recently in public policy deliberations on the future of social security in the USA [27].

Rauschmayer and Wittmer compared different participatory process models for implementing AD in communities [10], ranging from mediated modeling, participatory multi-objective decision-support modeling, a consensus conference, multi-criteria evaluation in participatory workshops and cooperative discourse, describing how the various process models address issues of:

	▪ Information and data quality, complexity and uncertainty;
	▪ Legitimacy of the deliberative process including involvement of multiple relevant actors and accountability for taking subsequent action;
	▪ The social dynamic during the deliberative process including agency of actors and transparency of the rules to arrive at consensus and/or demonstrate diversity;
	▪ The economic cost of the process itself relative to the problem at hand.

Cooperative discourse that includes elucidating values (e.g., decision-making criteria among the stakeholders), invitation of experts to provide technical input on agreed upon metrics, and a citizen jury to vote on various action options, appeared to provide the maximum AD benefits [10].

Barabas further describes methods for measuring changes in knowledge (from analysis), changes in opinion (from combined deliberative process) and ability to come to a consensus or to demonstrate diversity, arising from the AD process [27]. The work of Barabas, thus, provides a beginning template for evaluating learning and change in behavior/opinion arising from the AD process.

The elements deemed essential for good process implementation of AD are very similar to those identified in CBPR often published in the public health literature. CBPR is based upon three principles [11]:

	▪ That communities hold local knowledge that is as or more important than expert technical knowledge in designing and implementing public health interventions;
	▪ The local community has a right to be involved in decisions that impact their community;
	▪ The local community has a right to be involved with identifying solutions that impact their community.

Community-based participatory research is important because theory-driven interventions and policy-based solutions are found to be more effective when they meaningfully involve people in the community in developing local public health solutions. In a similar manner, AD and CBPR emphasize bringing many different people together including community members, researchers, and government officials to deliberate and come to a consensus between these diverse members. Both CBPR and AD have in common a partnership model between technical experts and local community knowledge. Goal and agenda setting, the selection of analytical tools, gathering and interpretation of data all involve both the ‘expert’ and the community in numerous joint workshop-like settings.

Elements of cooperative discourse, as seen in AD for environmental land-use planning [10], along with co-development and interpretation of data inherent in CBPR [11], have been combined with learning outcomes assessment methods [27] to design UC Denver’s Sustainable Communities Program, described in the following section.

Participatory process model for climate action planning & case study application

The process model developed at UC Denver to engage technical analytical skills drawn from academia with community deliberative processes for low-carbon planning, utilizes principles of AD and CBPR described in the previous section. The various steps and best practices in the process model evolved over time as the study team gained insights from working in two case study cities – Denver (CO, USA) (2005–2007) and Broomfield (CO, USA) (2009–11). The following steps in the process have now been identified:

	▪ Step 1: Identify the deliberative body in each participating community and, relevant to AD criteria, assess its composition, its legitimacy and its accountability for implementing actions;
	▪ Step 2: Co-develop data sets for baseline sustainability analysis: we address issues of complexity, data uncertainty and cases of data unavailability by encouraging co-development and co-interpretation of data as in CBPR, wherein academic institutions and community representatives jointly gather and benchmark energy use and GHG emissions in the cities;
	▪ Step 3: Set sustainability goals and elucidate various underlying values: in addition to quantitative and/or qualitative goals pertaining to GHG mitigation, elucidate what criteria are important to the various participants in prioritizing alternative sustainability actions;
	▪ Step 4: Facilitate a process to involve experts in scenario modeling of alternative sustainability actions in conjunction with deliberation. (e.g., mediated modeling and cooperative discourse) [10];
	▪ Step 5: Implement AD process model for prioritizing action: use a design-charette or citizen jury approach wherein groups of participants prioritize various actions based not only upon the technical experts’ models, but their own values and understanding;
	▪ Step 6: Demonstrate consensus or diversity in the final action plan by recording areas of majority/consensus agreement, along with a minority report if consensus cannot be achieved;
	▪ Step 7: Conduct outcomes assessment of the participatory process model, following the methods of Barabas to identify to what extent the AD-CBPR processes enhanced knowledge [27], promoted change in policy opinion, and promoted consensus (or clarified diversity) in the AD process. This is accomplished by measuring changes in knowledge and policy opinion (if any) before and after the AD process (Step 5)

Each of the above steps is described further using case details of two cities we have worked with: Denver (CO, USA) in 2005–2007, and Broomfield (CO, USA) 2008–2011. Since the two case studies were conducted at different times, and in fact provided the learning experience to refine the process itself, not all steps were carried out in exactly the same fashion in both cities. Consistent with the principles of PNS, AD and CBPR, this article does not describe a static study comparing two cities, but an evolution of methods and learning gained from working with the two communities.

Step one: identifying the deliberative body & assessing its composition

The composition of the deliberative bodies that develop sustainability plans in cities varies considerably. For example, the deliberative bodies range from 20 to 40 member task forces convened by the Mayor or by the City Council to smaller study groups of 4–5 persons established by City Councils. Sometimes task forces are designed to represent the community via broad-based invitations to participate, while sometimes they may consist of invited experts and some elected officials. In a few cases, they may develop spontaneously by self-selection of interested citizen-members from the community at large (e.g., small citizen committees established in towns such as Lafayette [CO, USA] and Golden [CO, USA]). We evaluated the task forces in Broomfield and Denver based on the criteria expounded in previous research [10] (i.e., composition, legitimacy and accountability).

Denver was among the first cities in Colorado to develop a CAP. The plan was developed under the aegis of Greenprint Denver, a special office for Sustainable Development established in the Mayor’s Office. A Greenprint advisory board was established and charged with the task of developing Denver’s CAP through a deliberative process that began in September 2005 – ending with the release of the plan in October 2007. The Greenprint advisory board consisted of over 30 members with diverse interests including local businesses, non-profits, universities, the local school district, foundations, contractors, City Council, the Environmental Protection Agency, local experts in sustainability, and city employees from several different departments [28]. Greenprint Denver and its advisory board have legitimacy, since they were directly charged by the Mayor to accomplish this task. The Mayor had established the goal of reducing Denver’s per capita GHG emissions by 10% by 2012, and hence a goal was pre-established in this case. In terms of accountability (e.g., who will be accountable for implementing and tracking plan progress), the accountability appears to rest jointly with Greenprint Denver and the Department of Environmental Health, which was charged with gathering data to conduct sustainability assessments, to track performance of individual actions, and to periodically update Denver’s GHG inventory after 2005.

The City Council of Bromfield, CO appointed a Sustainable Community Task Force with the charge of revising the Environmental Stewardship section of the 2005 Comprehensive Plan. This task force consisted of 30 diverse community members representing the five wards, city council, other citizen committees as well as educational, religious, business, health, and economic interests [29]. Community members were required to apply to the task force, and were then appointed by the City Council, providing legitimacy to the group. Invitations to participate and apply for task force positions were distributed widely via household utility bills and flyers posted at various community centers and libraries. The task force participated in a series of monthly meetings for 2 years to develop the sustainability plan. Accountability was unclear since it was not known if any particular entity was responsible for frequently updating Broomfield energy use and GHG emissions or tracking the specific recommendations of the comprehensive plan update. However, this may also be owing to the fact no specific quantitative goals were established in the comprehensive plan update process.

In Broomfield, the University had the opportunity to survey the task force and a sample of the community-at-large about their sustainability priorities and values. To survey the community-at-large, 1000 postcards were mailed to residents sampled randomly from water billing lists, which invited community members to respond to an online survey on sustainability in their community. Respondents were asked to what degree they agree with the importance of the following sustainability features shown below. These sustainability features were coded to represent their valuation of the three P’s of sustainability [103]: people, prosperity, and planet as shown in square brackets below:

	▪ Preserving the quality of life for future generations (general sustainability definition);
	▪ Balancing development of new homes, neighborhoods and businesses with protecting open lands [planet];
	▪ Preserving environmental quality (e.g., clean air and water) [planet];
	▪ Increasing community participation in decision making [people];
	▪ Promoting economic development through commercial and industrial activity [prosperity];
	▪ Conserving scarce natural resources (e.g., water, open lands, coal and oil) [planet];
	▪ Being prepared for unexpected events (e.g., fires, droughts, recession and acts of terrorism) [prosperity];
	▪ Promoting social equity so that all people have access to essential goods and services [people];
	▪ Ensuring affordability of basic needs (e.g., food, water, energy and shelter) [prosperity];
	▪ Reducing waste [planet];
	▪ Preserving a small town feel [people];
	▪ Reducing unemployment at the local level [prosperity].

Survey respondents were asked to respond on whether they agreed or disagreed to the above sustainability features. The scale of responses ranged from -2 (strongly disagreed) to 2 (strongly agree). Results were then aggregated into those that related to people, prosperity or planet. Figure 2A shows that the features relating to the planet were significantly (p = 0.00 for people vs planet vs prosperity and planet) more important to sustainability than those relating to people or prosperity for both groups – the task force and the community at large.

In the next question, respondents were asked to allocate 30 total points between people, prosperity and planet, based on the importance of each of the P’s in their everyday life. In this question, all three P’s were valued somewhat equally, with planet being slightly higher than people or prosperity as seen in Figure 2B. While the task force differed somewhat from the general population demographically (e.g., older and higher income), there was no significant difference among sustainability priorities between task force and the community at large (Table 1). Interestingly, both these groups valued environmental sustainability (i.e, planet) higher when asked about particular features (Figure 2A) versus conceptually allocating points between people, profit and planet (Figure 2B).

Best practices learned

By using surveys in Broomfield we identified two helpful practices:

	▪ Illustrating the similarities between task force members and the community at large increases confidence that the decision makers (task force members) share similar values with the community at large;
	▪ Using both qualitative and quantitative data on rating sustainability features is helpful to show how community values sustainability and the three P’s in a practical manner (rather than by conceptual allocation of points).

Process & Timeline:

Both Denver and Broomfield adopted a ‘Cooperative Discourse approach’ in which a facilitator led the deliberative group in their discussions and invited technical experts – some from the University – to offer technical and baseline analyses. The deliberative body then voted on the recommendations of the group. Greenprint Denver held a series of monthly meetings in Denver over a period from September 2005 to October 2007 to create the CAP, which consisted of the task force’s final recommendations to Denver’s Mayor. The Broomfield Sustainability Task Force met monthly starting in April 2009 until June 2010, when the plan was presented to the City Council. Suggestions for improvements were made and the plan was accepted by the City Council in January 2011.

Step two: co-development of data sets for baseline sustainability analysis

Both Denver and Broomfield co-developed data sets and engaged in baseline energy assessment with technical teams from UC-Denver. Indeed, the primary data gathering was the task of the communities themselves who were often tasked to work with various departments in the city, (e.g., water data from the Water and Wastewater Divisions), which enhanced the trust in the data. The task forces were also provided with data for review and comment and often challenged the findings and requested further analysis/reconfirmation. Benchmarking energy use data in buildings, transport and material use sectors was also useful in increasing trust in the data, as individuals could now compare various energy use metrics with their own homes and communities, other cities, and with state averages (Figure 3); for example, both Denver and Broomfield’s household energy use (natural gas and electricity) were within 5–15% of that reported statewide; Broomfield’s electricity use was approximately 0% higher than the statewide average (and Denver’s average), a finding explained by the local features in each community, for example, larger home sizes, more occupants and also greater percentage of single-family homes. Following the benchmarking of energy use data, the community-wide GHG foot printing were computed using methods and models published elsewhere [5,8].

Best practices learned

Starting with Denver, the team learned about several data sources to benchmark energy use data in communities, which not only increased trust in the data at the local level but also set standards for benchmarking in the peer-reviewed literature. The Denver study led the way in energy use benchmarking in US cities for multiple infrastructure sectors [8]. Another lesson relates to dealing with uncertain data or data that were not directly gathered at the local level. As shown in Figure 3, these data were color coded in red to represent poor data quality, a particularly useful practice for data on municipal solid waste that were unavailable at the city-level and had to be estimated from state-level statistics.

Step three: setting sustainability goals & elucidating various underlying values

Cities in Colorado differed significantly in how they articulate sustainable energy and low-carbon goals. A few cities such as Denver and Golden articulated numeric targets; for example, Denver’s climate action goals include reducing per capita GHG emissions by 10% by 2012 and an absolute reduction target of 25%, to get the entire community below 1990 levels by 2020. The City of Golden supports the goal of 20% reduction in 2007 community-wide emissions by 2017 and has adopted numeric goals in specific sectors (e.g., 90% of new buildings should be built to green standards, reduce community energy consumption by 20%, reduce solid waste stream by 25%, decrease vehicle miles traveled by 15%, and reduce per capita water consumption by 15% in 5 years).

However, many cities, adopt more qualitative goals related to general conceptualization of sustainability; for example, Broomfield as part of its revision to the comprehensive plan, set more general principles in categories such as resource conservation, renewable and alternative energy, community education, economic and financial sustainability, and transportation. Some prioritized principles include requesting data from trash haulers in order to explore waste reduction policies, distributing a sustainability section of the ‘Welcome Packet’ to new residents, and considering the development of programs that encourage energy efficient ‘green building’ practices.

Owing to this wide variation in goal-setting across various cities, the values that the deliberative body will use to evaluate various sustainability actions were elucidated. These values can be expressed in the form of decision-making criteria, thus the task force members were queried on a diverse range of factors that would play a role in their decision-making for prioritizing sustainability actions of the future.

Task force members in Denver, were queried in a group setting to identify the various criteria that they would use to prioritize alternative CAP strategies. Their answers in 2005–2006 revealed the following factors as important (in no particular order):

	▪ Effectiveness in reducing GHG emissions overall (carbon impact);
	▪ Cost–effectiveness, including both first cost US$/metric tons CO2e and life cycle savings represented as a payback period;
	▪ Degree of public engagement;
	▪ Ancillary benefits, such as water savings and health benefits;
	▪ Ease of implementation;
	▪ Political feasibility of the actions;
	▪ The ‘butterfly’ or multiplier effect on other communities, demonstrating Denver as a leader.

When a different sustainability task force was convened for Broomfield in 2009, it provided an opportunity to formally survey task force members on the relative importance of the above decision-making criteria. Task force members also had a chance to offer additional criteria. Results shown in Figure 4 indicate that all these criteria are important – that environmental, social and economic benefits, as well as practical aspects of implementation, are critical when making decisions on sustainability policies or programs. Furthermore, all the criteria received fairly high and similar average scores, but there was great variation in how task force members rated the importance of each criterion. Criteria within the circles in Figure 4 were not statistically significantly different from each other, while those circled toward the left were identified as more statistically significant than those to the right. The high average rating awarded to all criteria and the significant variation in rating each individual criterion illustrates the challenges in prioritizing sustainability actions. However, such detail can show the task force what is of highest importance and make their priorities transparent to all.

Step four: facilitating a process to involve experts in scenario modeling

University teams provided analytical data on alternate carbon mitigation scenarios for facilitated discussions of the task force during monthly workshop formats. The alternate scenarios were structured for the task force, covering a range of actions ranging from ‘do nothing’ to more challenging policy actions, as listed below:

	▪ Do nothing;
	▪ Establish an information clearing house (website) on available water/energy programs;
	▪ Offer environmental education campaigns;
	▪ Provide financing programs (e.g., low-cost loans for energy efficiency and renewable energy upgrades);
	▪ Implement mandates (e.g., residential energy conservation ordinances requiring homes to follow basic energy efficiency standards);
	▪ Implement tiered rates for high energy and water consumers;
	▪ Establish new energy efficient building codes to address new building stock;
	▪ Adopt smart growth policies;
	▪ Support market-based programs (e.g., pay-as-you throw waste programs).

Based on lessons learned from the Denver Case Study, we included community participation rates in a range of these actions, from ‘do nothing’ to voluntary outreach to mandates, in order to demonstrate the impact of these various ‘action scenarios’ in each of buildings, transport and materials-waste sectors.

These scenario-based models describing the impact of voluntary action (e.g., educational campaigns with low interest loans) versus mandates (required home energy upgrades at time of sale) yield carbon mitigation wedges for the community of interest under various policy-program scenarios [Unpublished Data; Ramaswami A et al.], inspired by the Princeton Stabilization wedges [30]. The details of the model are beyond the scope of this study, and are reported elsewhere [Unpublished Data; Ramaswami A et al.].

The wedges are illustrated conceptually in Figure 5, with each wedge representing an action. For each wedge, additional analytical data are gathered to inform the various decision criteria, such as, water saving, cost savings, expected participation rates and payback period [Unpublished Data; Ramaswami A et al.]. The analytical data are then used in a design charette or citizen-jury deliberative format wherein groups of task force members provide feedback on the action options in various ways, as described in the following section.

Step five: implement AD process model for prioritizing action

The community-wide sustainable energy and carbon stabilization wedges developed from technical analyses incorporating co-developed data inputs, were then used in workshops organized periodically around each infrastructure sector – buildings, transport and materials-waste.

In Denver, the AD workshops were held with the Greenprint Denver members monthly – actions were presented on large posters showing the various benefits and co-benefits. Groups of task force members gathered around these posters, deliberated with the technical analysts about the underlying data and among themselves about the political feasibility and practical implementability of the various actions. Task force members were then asked to assign a qualitative rating reflecting their subjective judgment about proceeding with these actions by placing ‘dots’ (stickers) alongside each action:

	▪ Green dot: green signal to proceed, prioritize for immediate action;
	▪ Red dot: absolutely not to do (mostly as these actions were deemed politically infeasible);
	▪ Yellow dot: hold for further re-evaluation.

Task force consensus developed into the CAP, which was then shared with the community in more than six open-house meetings.

Broomfield had monthly task force meetings where the members identified five sections of the plan and each sub-group discussed potential actions within these sections. Results of these small group discussions were brought back to the whole group where actions were deliberated. Broomfield then held an open house in March 2010 for the community where task force members made posters with proposed actions and community members were able to comment by placing notes on the boards. Task force members engaged with community members with how they came up with the actions and why they felt they were important. The task force members then took the community comments into account and revised their proposed actions accordingly.

Best practices

The red dot method used in Denver is better for documenting the decision process over time. Beyond the signal to proceed or not, a shared understanding of key barriers that inhibit or that facilitate adoption of various strategies, which is of great value to communities.

Step six: demonstrating consensus or diversity; plans for review

In Denver, after Greenprint Denver held monthly sector-specific meetings, the task force deliberated for an additional 3–4 months, asking for additional information and details. They then wrote a draft action plan that incorporated all infrastructure sectors and provided a holistic vision of a CAP for Denver [28]. Greenprint members voted on the final action plan – deliberations continued until they achieved consensus on most aspects of the plan. The votes were recorded, along with one minority opinion that suggested the CAP seriously consider nuclear energy in addition to the other GHG mitigation measures proposed in the CAP. The draft CAP was presented in various neighborhood meetings and posted online to gather public input. The final plan was adopted in October 2007. Various proposed actions were then codified into city law (e.g., green concrete) by Executive Order 123 in 2007. Denver has re-evaluated its GHG emissions and tracked progress in CAP since 2005 with GHG updates cattied out in 2007 and 2009. Greenprint now sets goals for each year and still meets monthly to re-evaluate progress.

After Broomfield held the open house for the community, the task force met again twice to revise the plan in line with the suggestions from community members at the open house. The sustainability plan was available for public comment, after which the task force presented the draft sustainability plan to the City Council in June 2010. Since the plan contained a lot of material, there were many questions for clarification from the City council and a study session was held in October 2010. A smaller group of the task force made revisions to the plan and presented the revised plan to the City Council in November 2010. A second public hearing was held in December 2010 and the council tabled the resolution for the plan adoption to the January meeting. The Sustainability Plan was adopted by the City Council on January 25, 2011 as Resolution 2011–28. Throughout the process and as the plan was revised, several action items initially suggested by the task force in the plan were then removed. Those items discussed by the task force, but not included in the plan were also made available for public review.

Best practices learned

Keeping task force members engaged not only during plan-making, but in implementation and various program evaluation is important. Setting goals (as in Denver) may emerge as a good method to engage the task force and community over the long term.

Step seven: outcomes assessment – measuring policy opinion before & after AD

UC Denver teams had the opportunity to conduct outcomes assessment of the AD process for the Broomfield case in 2009–2010. Surveys were distributed to the task force before and after the deliberating process in order to measure changes in knowledge and opinions regarding different types of policies. Two unique and anonymous identifying questions (e.g. mother’s maiden name and city of birth) were queried to link each task force member anonymously before and after survey. Since the goal of surveying before and after was to understand whether and in what ways learning occurred and opinions changed among task force members as a function of the AD process, the small sample size of about 20 task force members was not a concern. In addition, the open-ended questions included in the surveys provided a rich source of qualitative data that offered useful insights about factors affecting the AD process.

Figure 6 shows how the group changed policy opinion before and after the process. The changes are not dramatic across the whole group. Less controversial actions such as providing informational websites, educational programs and low interest loans, became less desirable post AD, likely owing to discussions about their dubious impact, (i.e., many of these actions such as developing an informational website are easy, but their effectiveness is questionable). The other changes in opinion were small. Since only half the task force provided both survey data before and after AD, we are careful to interpret these findings with caution. However, these methods highlight the utility of conducting well-designed pre- and post-surveys to understanding learning and opinion change as a result of a participatory AD process. It is worth noting significant attrition of the task force members themselves over the 18-month process, anecdotally citing the difficulty in making the time commitment over such a long time period. Such feedback points to the need to develop a more rapid AD process for sustainability and climate action planning.

To assess knowledge gained, task force members were asked to quantitatively describe the major areas of learning from the technical analysis. The results in Box 1 show that the majority of task force respondents (12 of 22) gained knowledge from the technical data provided during the process. Two respondents noted gaps in the data while one dismissed the analysis on the basis of distrust of the politics of climate change.

In the survey after AD, participants were asked about the importance of technical analysis and deliberation in their decision-making. For technical analysis, the majority (69%) found the technical data to be helpful in making informed decisions, while others felt they already had the knowledge necessary (17%) and a small percentage (12%) questioned the validity of some of the technical information. These results are consistent with open-ended comments noted in Boxes 1 & 2. In an open-ended answer section on deliberation, some respondents commented that the deliberation process opened their eyes to other points of views while other participants felt that some people in the group had firmly set opinions that would not change no matter how much deliberation went on (Box 2). These open-ended responses are important in that they can provide a foundation for applying different policy process theories to understand the political process of developing CAPs, for example, frameworks such as the advocacy coalition framework [31] may be appropriate given that issues of trust in the data based on political beliefs has emerged from the qualitative comments. The advoacacy coalition framework helps explain that learning by policy actors is shaped not only by technical information per se, but also by their prior beliefs and trust in that information and its associated knowledge networks and coalitions (e.g., media, scientists, special interest groups and so on).

Conclusion: identifying best practices

Using learning and insights from our two Colorado case study cities, this article describes the diversity of processes by which local governments engage with community members and with technical experts to develop sustainable and low carbon development plans for their cities. This diversity includes the size, selection and types of task forces charged with developing CAPs at the community level, as well as whether community plans proposed specific quantitative goals versus more general principles for sustainable development. However, even more remarkable is that despite this diversity, the case cities were able to effectively use a common process of AD in developing sustainability plans.

Based on learning from work with these and other cities, we offer evidence that a participatory model can be successfully used in a range of communities where technical information can be combined with deliberation to advance planning for sustainable and low carbon cities. The key elements of an effective analytic deliberative process include a broad-based task force membership that is engaged with technical experts through a facilitator. The group engages in analysis and deliberation to prioritize various actions, develops consensus and has mechanisms for reevaluation and revisiting GHG emissions and the progress of the plans within the communities. A few key observations about the AD process in case study cities are worth mentioning:

	▪ There is no ‘one size fits all’ model for engaging this work; each city must adapt the participatory AD process to fit its own cultural, social, economic and political circumstances. However, a common set of AD processes can be used by diverse cities.
	▪ This work takes a long period of time; for example Denver and Broomfield took 2 years, although it is possible to restructure participatory AD processes within shorter, more intensive blocks of time, the impact of this on learning and quality of decision-making is however, unknown.
	▪ The value of collecting and carefully reviewing technical data is critical, and involving community task forces in this process helps ensure that technical data are clearly understood, trusted and appropriately acted upon during the decision-making process and resulting sustainability plan.
	▪ Elucidating decision-making criteria, opinion and measuring changes in knowledge and policy opinion before and after is shown to be useful in understanding the AD process.
	▪ Quantitative goal setting and continuous program tracking by cities appears to be useful for engaging task forces over several years.
	▪ Various theories drawn from public affairs (e.g. ACF) are needed to further understand how learning and policy opinion change occur.

The AD process is currently being implemented and evaluated in several communities in Colorado in conjunction with the University of Colorado’s Sustainable Communities program. The goal of these efforts is to continue to learn and adapt an efficient and effective AD process, which includes collection of data on the learning that occurs among task force members (e.g., changes in knowledge and opinion) and how that influences consensus. As this work continues, we have identified the following areas that need to be improved upon is future methods for measuring both the process and outcomes of this work:

	▪ Improve the content of surveys designed to measure the demographic background, values and beliefs of task force members and the community at large; for example, to specifically identify their belief in human-made climate change. Such data can reflect the range and common perspectives about sustainability held by task force members and the community;
	▪ Replicate and improve survey methods designed to measure changes in knowledge and policy opinion among task force members before and after they participated in the AD process. The survey has been tested only once and requires improvement, particularly in uniquely and anonymously linking survey responses before and after AD.
	▪ Future studies may also include survey questions that ask task force members explicitly to allocate a fixed number of points between the analytic process, the deliberative process, and their own prior knowledge and beliefs. This will help us better understand what is guiding these deliberative bodies.
	▪ Finally, while the task force members rated various criteria for decision making, the overarching goals may be so different across communities that these criteria may not be equally relevant for all deliberative processes; for example, quantitative data on benefits and costs of energy and greenhouse gas mitigation may be important in communities such as Denver that have articulated quantitative goals in these sectors, whereas such criteria may not play out when more general sustainability and greening-the-community goals are operational in other communities.

Analysis of the above factors will be useful to uncover political drivers and shed light on the learning process for developing low carbon sustainability plans in communities of different types.

Future perspective

Over the next 5–10 years, the participatory process-model is envisioned to mature into a long-term outreach program wherein technical teams from academia are trained to partner with communities both to develop sustainability plans and to assess collaborative learning.

Table 1.  Demographic data showing that sustainability task force in Broomfield was older, more affluent and had lived longer compared with the community at large.

	Broomfield population (%) (n=91)	Task force (%) (n=30)	Community at large (%) (n=91)
Demographics
Males	50	53	45
Females	50	47	55
Bachelor degree or higher	42	74	72
Lived in Broomfield > 6 yrs	N/A	80	57
Household income
<US$49,999	33	12	20
$50,000-$99,999	36	31	38
$100,000-$149,999	18	27	29
>$150,000	13	30	13
Age
<34 years	22	14	18
35–54 years	33	41	60
>55 years	16	45	22
Race or ethnicity^†
White or Caucasian	89	97	93
Latino or Hispanic	6	3	1
Asian or Pacific Islander	6	0	4
American Indian, Inuit or Aleut	1	0	2
Black or African–American	<1	0	2

^†Total may not add up to 100% if respondents identified themselves as more than one race.

Box 1.  Open-ended responses to what task-force members in Broomfield learnt from technical data^†.

Gained knowledge from technical data

▪ Sustainability actions/opportunities learned ▪ Conservation efforts already in place in Broomfield ▪ Integration of all aspects of city and county ▪ Figures and outcomes of proven techniques that worked or did not work ▪ Different sustainability terms ▪ Amount of CO2 equivalents it takes to make concrete ▪ How big of an issue sustainability is ▪ Water and energy ▪ The magnitude of emissions Broomfield alone emits

▪ Sustainability challenges learned ▪ People and groups who are making small changes ▪ Politics play a large roll in the process ▪ Difficult to change public opinion and policy ▪ Costs and political realities of different proposed policy options ▪ Need to implement multiple actions to make a significant difference

Noted gaps in technical data

	▪ Not enough emphasis on sustainable economic development and workforce issues
	▪ Not all data to help with discussions was provided

Dismissed technical data

▪ Most of the sustainable information came from liberal publicists rather than a wide range of scientific researchers

Box 2.  Open-ended responses to the role of discussions in decision-making^†.

Discussions viewed as useful

Could see different perspectives of other task force members, municipal employees and stakeholders

Learned from hearing others’ opinions

Discussions were lively and showed a good degree of flexibility

Allowed for different points of view to be heard

Small group discussions were extremely beneficial while larger group discussions were almost as important

Expertise of different task force members was helpful in understanding and supporting existing or proposed sustainability policies

Discussions were more rational and enlightening than technical data

Discussions were important to bringing the group to consensus

Discussions viewed as limited

Came to a consensus but it was too conservative

In the end people took hard-lined stances based in politics rather than in good science

Discussions provided a limited role in decision-making

Too much of a skeptic of fad topics

Analytic deliberation

Process of scientific analysis within the democratic deliberative process.

Community-based participatory research

Technical experts and community members work together to identify solutions that impact their community.

Participatory process

Active participation of all members of a group in a decision-making process. In this case, representatives from academics, local governments, and community members were actively involved in sustainability plan making.

Low carbon planning

Planning with the goal of reducing carbon emissions.

Executive summary

▪ Using two case studies, we describe a participatory process in which faculty–student teams from academia work with local governments and communities to co-develop climate action plans in cities. The participatory process model is based upon concepts variously referred to in different literatures as informal science, analytic deliberation and community-based participatory research. The participatory process includes seven steps: - Identifying the deliberative body in each participating community by its composition, legitimacy and accountability for implementing actions; - Co-developing data sets for baseline sustainability analysis by collaboration between technical teams and local government, addressing issues of complexity, data uncertainty and data availability; - Elucidating sustainability goals and underlying criteria for prioritizing alternative sustainability actions; - Facilitating a process to involve experts in scenario modeling of alternative sustainability actions in conjunction with deliberation; - Implementing analysis and deliberation for prioritizing action using a design-charette or citizen jury; - Demonstrating consensus or diversity in the final action plan by recording areas of majority/consensus agreement, along with a minority report if consensus cannot be achieved; - Conducting outcomes assessment of the participatory process model, to identify changes in knowledge and in policy opinion, and ability to arrive at consensus (or to clarify diversity) in the process.

▪ The process model is developed and refined over two case study applications in Denver (CO,USA) (2005–2007) and in Broomfield (CO,USA) (2009–2011) to reveal best practices for combining technical analysis with community participation for sustainability plan-making in cities.

Financial & competing interests disclosure

This work was supported by various grants and contracts including: GAANN grant (Grant Number P200A030089) from the US Department of Education, project contracts from the City and County of Denver, the City and County of Broomfield and the National Civic League, an IGERT award from the National Science Foundation (Award No. DGE-0654378), and a gift from the Wal-Mart Foundation. The authors have no other relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript apart from those disclosed.

No writing assistance was utilized in the production of this manuscript.

Notes

^†A total of 15 out of 22 respondents answered this question.

^†A total of 16 out of 22 responded.