Challenges of consultant-led planning in India’s smart cities mission

Abstract

The growing involvement of private-sector consultants in urban planning has been critiqued as a potential problem, mainly due to doubts over their ethical position. India’s Smart Cities Mission which aims to equip 100 cities with smart technologies, relies on private consultants both to plan the interventions and to implement them. With the planning phase now complete, and implementation in its early stages, this study examines the proposals generated by the consultants. The study deploys natural language processing computational techniques to compare a large corpus of text extracted from the proposal documents to a framework of common planning terms. The analysis yields insights regarding the consultants’ “styles,” and the evolution of the proposals over four rounds of selection. Findings suggest that some consultants show better results than others, but as many as a third of the reports prepared for the mission have low scores on the study’s metrics. In addition, a close reading of the program design helps understand the institutional context within which consultants are embedded. The paper concludes with recommendations for closer scrutiny of the consultants’ work within the mission.

Keywords

Smart cities urban planning machine learning natural language processing consultants India

Introduction

Although foreign consultants were invited to contribute to various urban planning projects in postcolonial India (Banerjee, 2009), consultant-driven urban development took off in earnest after market liberalization in the 1990s, particularly for large scale privately constructed suburban greenfield developments, and large infrastructure projects requiring significant technical expertise (such as the Delhi Metro). The role of consultants in urban planning in India, particularly under the Jawaharlal Nehru Urban Renewal Mission (JNNURM), which ran from 2005 until 2014, has been recognized as an important moment in the unfolding of neoliberal urban governance across the country (Basu, 2019; Sadoway et al., 2018; Kundu, 2014; Ghertner, 2010; Banerjee-Guha, 2009). Although JNNURM had already opened the door to increased reliance on private consultants to aid urban planning, the intensity of engagement with consultants, their relationship with the state, and the scope of their work have changed dramatically under the Smart Cities Mission (SCM). A wider trend toward consultant-reliant governance is now being seen across sectors in India (Singh, 2021).

As mandated by the SCM, consultants have been hired by all 100 cities, to make plans that involve direct interventions at the community or neighborhood scale. Labeled “area-based development,” this component of the proposal typically covers less than 10% of the area of the city, while utilizing 80% of the budget (Anand et al., 2018). This has profound implications for local government and participatory democracy at the local level. Consultant-led planning, in principle, promises the benefits of international cutting-edge expertise along with efficient utilization of public resources. The mass hiring of consultants, however, also raises the possibility of ethical conflicts, lack of substantive and sustained engagement with people, and the undermining of existing planning institutions (Wargent et al., 2020).

As this study demonstrates, the extensive use of consultants in the preparation of SCM proposals has created a variety of policy challenges, specifically around issues of accountability and quality control. Using the SCM proposals of the 100 selected cities, the study employs a natural language processing-based analysis of the text in the proposals. The methodology yields findings with regards to the “styles” of the various consultants and the homogeneity of proposals across cities and consultants. Further, a close examination of the SCM program design, with a focus on institutional aspects, reveals that the mission suffers from several significant limitations.

The paper is structured as follows. A brief overview of the SCM is followed by a review of the academic discourse on the mission so far. A review of current research on the use of consultants in urban planning sets up the framework for this study. This is followed by a description of the research design and methods. The Findings section then presents the results of the analysis. Theoretical implications are collated in the Discussion section, along with recommendations for program governance. The paper concludes with a note on the shortcomings of the study, and directions for future research.

Smart cities in India: An overview

The Government of India’s Ministry of Housing and Urban Affairs (MOHUA), formerly known as the Ministry of Urban Development (MOUD), launched the Smart Cities Mission (SCM) as a centrally funded initiative in mid-2015. A two-stage competition method to select 100 Indian cities was announced and cities were required to prepare proposals in a given format comprising five sections of fifty-eight key questions—city level information (including past efforts to improve the city); details of area-based development (ABD); pan-city initiatives; implementation plan and financing plan. Cities had the flexibility to provide additional information such as strategic vision, prioritized goals, partnerships, and rationale for approach to redevelopment. A baseline mapping and an assessment of the city’s current performance on key indicators were also expected. ABD was expected to focus on redevelopment of an existing part of the city into a “smart area” while pan-city smart solutions were few key applications of ICTs to improve local governance and delivery of public services. Cities were required to create a special purpose vehicle (SPV) to plan, implement, manage, and monitor the projects. A section that described plans for innovative means of funding the projects including merging projects from other government schemes, raising funds from the financial markets and self-funding by urban local bodies was expected. A total of 100 cities were chosen across five rounds. Cities rejected in the previous round were given an opportunity to improve their proposals for consideration in the subsequent round.

The academic discourse on India’s smart cities mission, its implementation and governance, focuses on three dominant perspectives. First, a technology-push perspective focuses on advances in IT for improvements in service delivery, efficiencies and through smart solutions meet citizen’s changing demands (Kumar et al., 2020). A second, more people-oriented, human-centric perspective suggests that the focus of smart cities should be to enhance quality of life of city’s residents through economic growth, optimal utilization of resources, sustainability and efficient mobility (Kummitha, 2018). The third, and more critical perspective, attempts to caution policy makers of the perils of privatization and corporatization which they believe may divide “its elite citizens from the rest of the population” (Khosla, 2018: p.69). This perspective questions the market-led, corporate-driven approach to urban planning and governance where “technocratic nationalism” meets the industry’s “technological utopia” (Basu, 2019: p.78). Praharaj and Han (2019) suggest that even the definitions of smart cities in India are influenced by the multinational corporations to suit their business interests. The Mission also has been critiqued for undermining institutions of local government (Das 2020; Chakrabarty, 2019; Anand et al., 2018; Aijaz and Hoelscher, 2015). Not least, Datta (2019, 2018, 2015) argues that the “smart” push is part of the larger effort to nurture the enumerable and accessible digital consumer-citizen, a view supported by Miklian and Hoelscher (2017). This growing body of work agrees broadly with the extensive literature, from around the world, that has critiqued the political-economic motivations behind Smart Cities. Datta and Odendaal (2019), for example, have suggested that smart cities play a role in “normalising the structural and social violence inherent in urban transformations” (p.387), while Cowley and Caprotti (2019) have characterized smart cities plainly as “anti-planning” (p.428).

Urban planning by private sector consultants

The hiring of private consultants for conducting studies and carrying out specific tasks related to urban planning grew rapidly in the 1980s (Higgins and Allmendinger, 1999; Mitchell, 1995; Saint-Martin, 2000), particularly in advanced economies. By the end of the century, concern was being expressed regarding the use of consultants from perspectives of ethics, efficiency and hegemony (Campbell and Marshall, 1998; McCann, 2001; Mitchell, 1995).

In recent years, the widespread use of consultants has again been attracting critical attention, not least because of the evolving role of the private sector in urban planning, and the ensuing power struggles and ethical dilemmas. Private consultants are hired in the urban planning sector mainly for economic-institutional reasons, such as a lack of capacity at the local level (Loh and Norton, 2015; Momani and Khirfan, 2013), particularly for specific technical studies, and to save the time of local planning officials. The nature of the planning profession is becoming increasingly technical with specialized analyses and reports becoming a requirement for various permission processes. Rather than hire more professionals as public officials, which is perceived as wasteful, elected officials prefer to create opportunities for the private sector to take over functions that were formerly carried out by government departments (Raco, 2018; Sager, 2011).

Further, as the construction and infrastructure sector grows around the world, an increasingly wide array of tasks and functions are being outsourced to private consultants, giving rise to serious misgivings. Concern regarding planning consultants arises mainly from doubt over their ethical positions (Lauria and Long, 2019; Linovski, 2019; Loh and Arroyo, 2017). Scholars have also argued that the involvement of private consultants can lead to a condition of “consultocracy” (Ylönen and Kuusela, 2019; Khan et al., 2018; McCann, 2001), and compromise the credibility of planning processes and the quality of planning outcomes (McLeod and Schapper, 2020). It is worth mentioning in this regard that foreign agencies, such as Germany’s GIZ and Japan’s JICA, perform vastly different roles than private consultants and are not the subject of this study.

A growing body of work is examining also the new dynamics of consultant-led urban planning, based on the experience of developing countries (Bock, 2014; Mouton, 2021; Rapoport and Hult, 2017; Shatkin, 2008 among others). The problem of international consultants contributing to what Roy and Ong (2011) call “worlding” of cities has also been noted by others (Mouton, 2021; Rapoport and Hult, 2017; Robin and Brill, 2018; Shatkin, 2008). Some scholars have argued that consultants are also hired to serve political interests (Momani and Khirfan, 2013). Alberto Vanolo (2014), for example, has argued that the “smart cities” paradigm employs consultants to help depoliticize the urban question. In addition, it has been argued that the use of consultants is but one component of the overall privatization of the city and the growing preeminence of neoliberal urbanization (Campbell and Marshall, 2000; Jones and Comfort, 2019; Prince, 2012; Sager, 2011). Anne Vogelpohl (2019) has problematized urban planning by management consultants in the context of German cities. Her findings form the basis of our theoretical framework (described below).

Research design and methods

Theoretical framework

McLeod and Schapper (2020) have brought attention to the urgent need to assess the quality of the work of private-sector consultants working in the field of urban planning. Drawing on the work of other sources, they propose a typology of four categories of measures of quality (ibid, p.4). The categories may be summarized as “context” (strength of systems, institutions and staff), “process” (strength of procedures), “deliverables” (quality of the plan itself), and “outcomes” (actual performance of the plan). These categories constitute the first part of the theoretical framework adapted for this study. Given the nascent stage of the SCM, “outcomes” is difficult to gauge primarily because not many projects have so far been implemented. The winning Smart City proposals, however, do provide a window into two categories of quality—“process” and “deliverables.” Further, based on a critical reading of the overall program design, some comments can be made about how the context is affected by program design.

To analyze the category of “context,” this study draws on Vogelpohl’s three “tensions” (2019, p.111) that characterize the involvement of management consultants in urban planning. The first tension—“between global and local”—emanates from global consultants drawing legitimacy from their international networks, and using “the appearance of being appropriate assistants for steering growth-oriented urban development” (p.112) to enhance their “local convincing power” (ibid). The second tension—“between style and knowledge” (p.111)—refers to consultants’ propensity to reduce complex political urban questions to projects, with very little value being added in terms of new ideas, or in terms of substantive improvement of processes. The third tension—“between exclusion and inclusion” (p.111)—whereby consultants tend to ignore certain voices, and focus on “those aspects of the urban that promise future growth” (p.112).

Research Questions

Research questions correspond to the theoretical framework described above, and reflect the exploratory nature of the project. The first research question focuses on “process” and “deliverables”: 1. How does the use of consultants affect the quality of plan-making? The second research question aims to understand the “context” of plan-making. 2(a) How do consultants fit within the institutional context? 2(b) How does the selection of consultants reflect the mission’s outlook toward planning?

Analysis

Content Analysis

Research question 1 was approached through a technique of content analysis using machine learning (ML). The use of such techniques is not uncommon especially in analyzing literature and large volume text in thematic areas such as social media, health care, e-commerce, political discourses, legal case files (Vajjala et al., 2020). It is important to note that the consultants and associated city names have been anonymized. Although it appears from the Mission’s guidelines (MoUD, 2015) that only one consultant was to be hired by each city, to help with the preparation of the Smart City proposal, it appears from the proposal documents that in many cities, multiple consultants contributed to the preparation, revision, and polishing of the Smart City proposal. Therefore, it would not be fair to attribute the results from any city to one consultant, or to assess the performance of consultants by name.

We designed a method through which 100 Smart City Proposals could be analyzed in a manner that was convenient (automated) and consistent (facilitated comparison across the 100 proposal reports). A reference framework of terms, that reflect established important ideas in the practice of urban planning, is compared to the text of the Smart City Proposals. The reference framework consists of 120 terms—4 primary (first-order) themes, with 6 sub-themes each, and 5 words for each sub-theme (as shown in Table 1). The four primary first-order terms in the reference framework, represent “procedure” (Participation and Stakeholders) and “deliverables” (Goals and Interventions) drawing from “process” and “deliverables” of McLeod and Schapper (2020).

Table 1.

Final framework of themes, sub-themes and terms (4x6x5).

Goals	Participation	Stakeholders	Intervention
Liveability	Online	Citizens	Infrastructure
Liveability, safety, convenience, recreation, clean	Online, internet, Facebook, Twitter, Email	Citizens, public, taxpayer, voter, resident	Infrastructure, roads, housing, transportation, parks
Equity	Survey	Builders	Services
Equality, disabled, gender, slums, inclusive	Survey, interview, questionnaire, sample, poll	Builders, construction, contractors, architects, estate	Services, water, waste, sewage, police
Innovation	Meetings	Governments	Financial
Innovations, startups, technology, solutions, patents	Meeting, workshop, discussion, forum, participation	Governments, ministry, municipal, state, department	Financial, revenue, funding, bonds, toll
Development	Experts	Businesses	Information
Development, jobs, tax, economic, production	Experts, conference, seminar, consultation, specialists	Businesses, enterprise, retail, wholesale, factories	Information, governance, software, digital, documents
Empowerment	Contest	NGO	Training
Empowerment, awareness, decision, democratic, decentralization	Contest, competition, event, outreach, prize	NGO, nonprofit, welfare, charitable, aid	Training, skills, vocational, livelihood, employment
Environment	Media	Suppliers	Policy
Environment, wetlands, pollution, floods, green	Media, newspaper, television, radio, print	Suppliers, companies, vendors, providers, chain	Institutional, rules, regulations, plans, laws

The reference framework was developed through an iterative process. To begin with, 24 terms were compiled in a bag-of-words (BoW). These 24 terms were compared across a small sample of cities to check the results of the algorithm. Three issues were noticed. First, the “bag-of-words” included some two-word terms (such as “economic development,” “social justice,” “service delivery” etc.). However, we noticed that model reliability increased when single word associations were used. We therefore eliminated two-word terms and adopted single-word terms. Second, comparing cities across 24 terms raised the possibility that cities could get similar scores while focusing on very different ideas. To improve the analysis, the BoW was reorganized into 4 primary themes and 6 sub-themes. Third, the associated words, contributing to the cosine score, were often unrelated to urban planning. So, the second-order terms were converted into a 5-word string for better accuracy. The use of 5-word strings has multiple benefits. The strings had better semantic coverage for cases where proposals use different words to refer to the same idea. The strings were exhaustive enough to cover the various meanings that are packed into each first-order term. The use of strings also reduced the risk of bias due to the overuse, or absence, of a single word from a proposal. This gave us the final framework of 4x6x5 terms.

Further, for greater accuracy, a 300-dimension space (in which vectors are trained) was used in GLoVe, instead of a 50-dimension space, as recommended by Pennington et al. (2014). This reduced the cosine scores, but the associated words with the highest cosine scores were a much better fit to the context. The expertise of the authors was relied upon to select the words, over several iterations. The four primary themes come from our interests in the planning of Smart Cities. All other terms were selected to attain a fuller meaning of the primary themes, while working within the limitations of GLoVe and the algorithm.

The GloVe Method

The GloVe method (Pennington et al., 2014) has been used recently in research on themes such as public emotions in the context of smart cities (Adikari and Alahakoon, 2021) and Islamophobia in social media (Vidgen and Yasseri, 2020) amongst others. GloVe allows users to map words to a dataset that contains pre-calculated values (cosine scores) for the relationships between pairs of words (on a scale of 0–1), based on co-occurrence of words in a variety of English-language sources. The GloVe dataset, which has a vocabulary of 400,000 words along with the measurement of the relationships between them, was developed at Stanford University and is available through open data commons license.

98 Smart City proposals were converted to text format, (two reports were not available in a format from which text could be extracted). Stopwords (a, an, of, the etc.) and images were deleted. Lemmatization (i.e., the process of converting a word to its base form) was then performed on the text to ensure that various forms of a word were converted to their root words. ML, operationalized through a Python-based code, was used to (a) derive a list of all words in the GloVe dataset (along with the respective relationship score) related to each of the 120 third-order terms in the reference framework (Table 1), and (b) to count the frequency of the occurrence, in each Smart City proposal, of each of the corresponding terms found in GloVe dataset. The code was able to generate a list of all word-pair relationships (between third-order terms and corresponding GloVe-recognized words), and the frequency of occurrence of each of the GloVe-recognized words. In order to generate a score of the third-order terms for each city, the following operation was performed on the list of relationship scores (cosine scores) and word frequencies, for each proposal

S c o r e o f a t h i r d - o r d e r t e r m = \sum c o s i n e s c o r e (i) * f r e q u e n c y (i)

A sum of the scores of all the third-order terms under each of the four primary themes was calculated, for each smart city proposal document. This allowed the comparison of scores for the four primary themes for each of the Smart City proposals—by city, by consultant, and by round of selection.

Further, city scores at the sub-theme level were also used to cluster the cities using the K-means clustering approach, an unsupervised ML algorithm that allows grouping of data points, in a manner that minimizes the dispersion within the clusters. K-means allows the classification of cities (into clusters) so that cities in the same cluster (on a particular dimension) are as similar as possible, while cities in different clusters are as dissimilar as possible (Kaufman and Rousseeuw, 2005). A plot of the scores and an exploratory analysis exhibited three clusters in most sub-themes. These scores were rolled up to the dimension level by summing them up. The elbow method (Shmueli et al., 2019) was used to find the cities’ scores could be categorized into 5 clusters. Cities with scores closer to 0 were further away from our framework of sub-themes and dimensions. Cities with higher scores are both closer to our framework and are likely to exhibit homogenous outcomes.

Analysis of program design

Research Questions 2(a) and 2(b) are answered based on an analysis of elements of program design. Publicly available documents including the Mission Web site (smartcities.gov.in), the SCM guidelines (MoUD, 2015), the template provided to the cities for preparation of proposals (included with the guidelines), and the “companion document” to the Mission guidelines (MoHUA, 2021), were analyzed. In particular, the following institutional aspects of involvement of consultants were examined: Selection, Accountability, Oversight, Roles, and Authorship (see Table 2). Figure S1 (in the Supplementary Materials) summarizes the method and findings of this study.

Table 2.

Analysis of institutional context of consultants in the SCM.

	Institutional aspects of consultant involvement	Literature	SCM situation
1	Selection	Leger et al. (2013), Devkar et al. (2013)	Bases for empanelment and fee structure not shared through mission Web site or media
2	Accountability	Vogelpohl (2019), Raco et al. (2016), Deas et al. (2000), Haughton (1999)	Details of the participation process, methods of analysis of data etc. remain unclear
3	Oversight	McLeod and Schapper (2020), Parker et al. (2018), Sager (2011), Flyvbjerg (2005)	No agency or specialists charged with quality control of the work of consultants (including processes, analysis and deliverables). Only an ex-post points-based evaluation of the final proposals, by the mission (selection process)
4	Roles	Stapper et al. (2020), Wargent et al. (2020), Linovski (2019), Higgins and Allmendinger (1999)	Decision-making process for the mass hiring of consultants, and the roles delegated to them, remains unclear. No debates on this aspect in the public realm before the mission was launched
5	Authorship	Faludi (2000)	No consultant is named in smart city proposals, in many cases multiple consultants have contributed to plan-preparation with no record of the role of each one. Authorship is fuzzy

Analysis and findings

Process and deliverables

The process of analysis described above yielded four findings. First, mapping the city scores across the four primary themes, a generic pattern was observed across cities and consultants. Second, similar mimetic outcomes were observed across some of the cities handled by the same consultant, although this effect was not uniform across consultants. Third, the smart cities mission competition was spread across a period of 2 years and five rounds. The city clusters show higher similarity scores among cities selected in the later rounds. Fourth, consultants fit uncomfortably within the institutional structure of urban governance, which raises more questions about consultants’ integration in planning practice in India.

Overall similarity of scoring patterns

As explained above, the analysis of the smart city proposals was conceptualized along a four-dimensional framework (represented by the first-order themes—Goals, Participation, Stakeholders and Interventions). These four themes focus on the procedural aspects of the planning process. Scores for first-order themes ranged from 42 to 206. City total scores ranged from 172 to 535 averaging 305, with a standard deviation of 78. It was observed that for all cities, the highest score was for the first-order theme “Intervention” and lowest for “Participation.” Scores for “Goals” and “Stakeholders” were similar and fell in between the other two. Table S1 (in the Supplementary Materials) demonstrates this for fourteen sample cities, which were chosen to represent all five rounds of selection, and to reflect consultants who worked on multiple city proposals.

The selection of terms in the framework of reference terms may have contributed to a near-uniform overall pattern across all cities. A more likely explanation, however, is that the requirement for the proposal to be submitted as a standardized form, was responsible for boilerplate effect. The form in question, issued by the Mission, was the mandated instrument for articulating and submitting proposals. The first section of the plan (City Profile) very briefly describes existing conditions in the city, along with several other topics (administrative efficiency, SWOT analysis, Strategic focus and blueprint, City vision and goals, Citizen Engagement, and Self-assessment). This section is typically about 14 pages in length. The remaining four out of the form’s five sections (accounting for about 78 pages) are devoted to proposing projects and explaining their implementation (Area-Based Proposal, Pan-City Proposals, Implementation Plan, and Financial Plan). Unsurprisingly, the words selected to represent the theme “Interventions” scored the highest—due to the presence of more relevant sections in the proposal form, resulting in higher frequency of those words. Similarly, although the proposal template mentions citizen engagement and participation, from the scoring schemes used, it appears, across both area-based development and pan-city solutions, citizen engagement was allocated amongst the lowest weightages, 5 out of 55% for the former and 1 out of 15% for the latter. This may have also resulted in the reduced attention to “Participation.”

Consultants’ Styles

Consulting firms that had prepared three or more Smart City proposals were identified for a comparative analysis. Based on content analysis, scores of the first-order themes for each city were plotted on four-dimensional radar charts to observe patterns graphically. The radar charts demonstrated similarities across consultants and across cities. Cities that were handled by the same consultant are plotted together on radar charts. In addition, the average scores for each consultant (i.e. the average of the first-order scores of that consultant’s cities) were added to the consultant’s radar chart. This four-dimensional radar plot of a consultant’s cities’ averages was taken as a graphical representation of the consultant’s “style.”

Inter-consultant analysis shows that although there was overall consistency in scoring patterns (i.e. highest scores for “Intervention,” lowest for “Participation”), consultants varied on the magnitudes of the scores (Figure 1(a)). Consultants’ styles fall into three dominant patterns. However, the three patterns were most distinct on three of the four dimensions—Interventions, Goals and Participation—than on the dimension of “Stakeholders,” where scores were more evenly spread. Since Participation scores were low in general, the lines appear to bunch together. The three bunches (groups), however, remain distinct.

Figure 1.

Sample of comparison of styles of consultants. (a) Comparison of styles of 10 consultants (b) comparison of cities of consultant Y (c) comparison of cities of consultant KK.

Intra-consultant analysis of the scores can help understand consultants’ approach to the planning process. In particular, this study is concerned with whether or not the proposals of multiple cities, handled by the same consultant, are similar or different. It was seen that there was no clear pattern with regards to this question. While some consultants had similar overall scores across the cities they had worked with (such as Consultant Y and M in Table S1 in the Supplementary Materials), some others (such as Consultant F and Q) had a greater difference between the scores of their cities. Also, some consultants had a strong signature style—that is, there was very little variation between the scores of the cities they handled (e.g. Consultant Y in Figure 1(b)). Others showed a greater degree of variation in the magnitude of the scores of their cities (e.g. Consultant KK in Figure 1(c)). Of the consultants with three or more cities, five emerged as the most diversely scoring consultants. This suggests that these firms were able to prepare plans that were tailored to local conditions. As explained above, however, the overall pattern of the four scores (indicated by the shape of the radar chart) remained consistent.

Evolution over time

Cluster analysis (as described in the methods section) showed that cities fell into five clusters according to their similarity scores for each of the first-order terms (dimensions). The five clusters were coded from 0 to 4 (indicating “very low,” “low,” “medium,” “high,” and “very high” similarity scores). This gave every city four cluster codes—one for each primary theme, as shown in Figure 2. Comparing the clusters across the rounds of selection showed a gradual increase in overall scores over time. Average scores across the four primary themes were found to improve gradually with each round of selection. The Fast Track round (held between Rounds 1 and 2) and Round 4 show the highest scores. This indicates a learning effect, over time, across all consultants.

Figure 2.

Change in scores across rounds.

In Round 1, a total of 14 cities were in clusters that were coded as zero (“very low” similarity scores) for at least one primary theme (as shown in Figure 2). Out of these, 8 cities were coded zero for all four primary themes. There were no zeroes in the Fast Track round. In Rounds 2, 3, and 4, the number of cities in clusters coded zero—for at least one primary theme—were 8, 10, and 1, respectively. Meanwhile, cities in clusters coded as 3 and 4 (“very high” similarity scores), for at least one primary theme, as a share of all cities in the round, rose steadily across the five rounds.

It is important to remember that these are similarity scores and therefore a high score does not automatically indicate a good or successful plan. Low scores, however, are a cause for concern, as this indicates that plans have a low linguistic similarity with a framework of 120 terms related to planning practice. As many as 33 cities (of the 98 studied) had a very low score in at least one of the four primary themes of the framework.

Institutional context and outlook toward planning

In response to Research Questions 2(a) and 2(b), this section reports an analysis of the category of “context,” derived from a critical reading of institutional aspects of consultant involvement in the SCM. The analysis focuses on aspects of Selection, Accountability, Oversight, Roles, and Authorship. Table 2 shows a summary of the analysis on the institutional context.

The process of selection of consultants is quite opaque considering the size and scope of the mission. It is not clear, for example, what competencies were considered important (Devkar et al., 2013) or whether much attention was paid to the interdisciplinarity of urban planning projects (as argued in the case of landscape architecture projects by Leger et al., 2013). Loh and Norton (2015) differentiate the process of selecting consultants as choosing between “responsive” and “independent” consultants, where the former satisfies the client’s politically oriented goals, while the latter provides independent objective analysis and produces plans that have a smart growth focus. In SCM it is not clear whether the selection process laid emphasis on consultants’ experience in projects involving public participation, spatial intervention in inhabited areas, working with communities, and environmental issues. The existence of social and economic networks, along with issues of heritage, attachment and memory, existing spatial practices, and a diversity of needs with attendant political motivations, makes planning in existing communities much more complex than the average greenfield project. Further, any large spatial intervention has to grapple with questions of environmental impact in urban ecological contexts that are already stressed, and, not least, issues of equity and spatial justice.

The list of 48 “empanelled” consultants and consortia (MOHUA, 2016) included 25 international consultants (most of them as members of consortia with Indian firms), of which most work mainly in the large infrastructure sector (e.g. AECOM, Arup, Dorsch, Haskoning, etc.), and the others are consultants in Real Estate (e.g. Jones Lang LaSalle, Lea Associates, Knight Frank, etc.), or in Management Consulting and Financial Services (including Deloitte, KPMG, PwC, EY, and McKinsey). The selection of firms with mainly engineering, management and finance experience may have happened simply because firms with those specializations have a bigger presence in India. Further, the country does not have many homegrown urban planning consulting firms. One must assume (since the process of empanelment of consultants has not been made public) that these consortia would have hired urban planners to establish relevant expertise. The question is not about whether consortia were able to cobble together competencies, but rather about how urban planning and community engagement have been viewed by the mission.

The question of accountability has been raised by several scholars, including Vogelpohl (2019) and Raco et al. (2016). Notably, the issue has been discussed also in the context of development corporations in the UK (Deas et al., 2000; Haughton, 1999), which are comparable to the SPV- and consultant-based design of the SCM. Details of public participation processes (schedules of meetings, minutes, photographs, recordings etc.) have not been documented systematically. Details of the analyses of participation feedback, along with analyses of economic and demographic data, are not available. In the absence of this information there seems to be a leap from public consultations to the final proposals. In this situation, there is no way to check if consultants were rigorous in their adherence to public opinions and public needs, and people cannot be sure about who is representing their interests.

Doubts regarding the consultants' work are compounded by the fact that there was limited oversight of their work while it was in progress. It may be assumed that working in an ecosystem of central, state and local agencies served this purpose to some extent. But no specific office was charged with the task of overseeing consultant performance in terms of processes, analysis and deliverables. This is particularly disappointing considering that the issue has been flagged repeatedly in existing literature.

As mentioned earlier, consultants have been trusted with significant functions including publicparticipation, spatial interventions in existing communities, land use and mobility proposals, andthe initiation of technologies, some of which have impacts on privacy. Even though the mission affects neighborhoods and communities in all major Indian cities, very little debate and discussion was carried out in the public realm regarding the planning functions that would be outsourced to private consultants, potential impacts, and strategies for control.

Not least, there is the question of authorship. The SCM marks a departure from the practice of the authorship of the plan lying with a local planning agency, which is established and empowered through the state’s Town and Country Planning Act. In the SCM, not only are the proposals submitted as a form (rather than as a planning report or plan document), but also there is no clear indication of authorship. As mandated by the Mission, consultants were hired specifically to help with creating the plan and have done most of the legwork. The SPV is the agency responsible for implementing the mission, and has few technical experts or elected representatives. And the final approval (before the proposal is sent to the Mission office in the Ministry) comes from the State government. The position of the urban local body, which was formerly responsible for planning, is now unclear. The urban local bodies are still involved, but they neither do the technical work, nor have authorship. The Smart City proposal, which lists investments and physical interventions in the city, has no clear author, no local public office that is responsible for its flaws or weaknesses (should there be any), and no system for quality control. Some local governments retained the function of approving proposals by setting out this condition clearly in the regulations that govern the SPV. It is not clear whether all 100 cities had the foresight to do so. Either way, the concern here is with the design of the program, which seems to be “blurring the lines of accountability” (Raco et al., 2016: p.233).

Discussion

Findings from this study suggest that the quality of planning within the Smart Cities Mission needs closer attention. As mentioned above, 33 cities had a low similarity score for at least one of the primary themes. Results from Round 1 are particularly stark as only one city (out of 19) recorded a high similarity score, and it did so for only one of the four primary themes. Low scores indicate a low level of correspondence between the proposal and the framework of 120 terms relevant to various aspects of planning practice. This disconnect between the proposals and planning vocabulary is worrying because it suggests that plans were made through an entirely different set of values and priorities. The scores do rise, on average, in subsequent rounds, but it is not clear whether that happened because of better planning or more careful writing of the proposals, under pressure from various specialists.

The findings resonate strongly with Vogelpohl’s (2019) theorization of the use of management consultants in urban planning discussed earlier. The first “tension” Vogelpohl describes, between “local” and “global,” is clearly visible in the number of international consortia empaneled by the mission. Not only is it the case that consultants draw legitimacy from their international brand and network, but also, in the case of the SCM, it seems the mission itself draws legitimacy from the presence and approval of international consultants, for both program design, and for how it repositions local planning agencies. The findings also show evidence of Vogelpohl’s second tension—between “style” and “knowledge,” and associated problem of the “projectisation of the urban” (2019, p.112). In the SCM, project implementation has indeed taken up the vast majority of the content of the template-based plans. The very design of the mission required city proposals to be developed as lists of projects, with little emphasis on new methods, knowledge, or ideas. With regards to the third tension (i.e. between exclusion and inclusion), it is too early to test whether the processes and projects have had the impact of excluding certain voices. It is certainly the case, though, that mission design has avoided many of the messy and challenging parts of urban development and jumped straight to project management.

The findings discussed above indicate a certain lack of concern with procedure, oversight, and ensuring substantive gains. The argument here does not question smart technologies per se, but rather draws attention to the political and contested nature of the implementation and administration of such technologies. As argued by several scholars (Raco et al., 2016; Robin and Brill, 2018; Stapper et al., 2020; Wargent et al., 2020; Zanotto, 2019 among others) the depoliticization of urban planning is achieved by its redefinition as a value-neutral, techno-scientific endeavor, ignoring extant complexities related to society, history and economic relations. The Smart Cities Mission, too, whether deliberately or unwittingly, forwards such an agenda. Meanwhile several important value-based decisions structure the program from the top, such as the reliance on consultants, the SPV model, project-orientation, proposals with homogenous structures, and a participation process that stops at the pre-planning stage.

The role of consultants, along with matters of oversight and accountability at the local level, are issues that have a profound impact on local government, democratic processes, and planning outcomes. A policy framework addressing these issues should be put in place immediately, in consultation with state and local governments. Further, changes should be made to the program design to make consultants accountable to citizens and elected representatives at the local level. This could be done by building capacity at the level of urban local bodies to hire and monitor consultants. In addition, an independent group of specialists and scholars could be tasked with the responsibility of systematically overseeing the processes, reports and deliverables. In addition, a close reading of the program design indicates that the SCM must lay a greater emphasis on transparency. Important aspects of the process of plan-making, including the bases for empanelment and selection of consultants, the work done by each consultant, the fees charged by consultants, documentation of the details of public participation exercises, analysis of feedback obtained from the participation process, analysis of demographic, economic and other data etc., ought to be documented systematically and made publicly available.

Conclusions

Shortcomings

The aim of this paper was to analyze the proposals prepared by different cities for India’s Smart Cities Mission, with a focus on the role of consultants. The analysis revealed severe limitations imposed by template-based plan-making mandated by the SCM. Despite generating valuable insights, this study is not without methodological limitations. An underlying assumption of the methodology is that higher frequency of certain terms implies greater attention to those ideas. The GloVe dataset allows comparison of individual words, rather than phrases or groups of words, resulting in a loss of contextual meaning associated with the use of a particular word. In addition, the degree of attention being measured by methodology remains indifferent to the kind of attention being given to the relevant ideas. So, if the terms are mentioned as part of criticism or rejection, they will still count toward the frequencies and similarity scores. It is expected, however, that this will not impact the results very much.

Although attempts have been made to ensure logical validity and independence of the sub-themes and words, it is inevitable that there will be some degree of overlap across categories given how linguistic markers are constructed and the proximity of associated terms as computed by the chosen algorithms. This inaccuracy, however, comes with such linguistic techniques, and is not peculiar to this study (Schuelke-Leech and Barry, 2018). Further, while a different combination of terms (or indeed, a framework with fewer or more terms, at each level) might yield different numbers, it is expected that the overall findings from the study will hold. Moreover, the mix of high and low scores across the levels, and across cities, and the multiple trends seen in the data, vindicates the design of the framework. It should also be added that since the process of plan-making within the SCM is quite opaque, it is not possible to be entirely certain about the possible influences acting upon the consultants.

Directions for future research

This study raises several questions regarding the consultants’ engagement with the Smart Cities Mission that require an in-depth qualitative examination. For example, it is important to inquire whether the gradual improvement in scores, between Round 1 and the Fast Track round, was due to substantive changes in consultants’ styles, or whether feedback from various quarters obligated consultants to draw from established planning practices and nomenclature. Future research must also investigate whether consultants, particularly those found to have a strong “style” regardless of the cities they handled, made substantively similar proposals for multiple cities. It would be interesting also to analyze if any relationships exist between the consultants’ primary area of activity and the plans they produced. Moreover, the question of whether or not consultant plans differ from municipal plans remains to be investigated, assuming comparable cases can be found.

Further, whereas this study offers a broad view of the terminology used in the proposals, a critical discourse-analytic reading of the proposals, and other documents released by SPVs and government agencies, could help generate a more layered understanding of the use of language in the proposals (as deployed by Basu, 2019 and Hoelscher, 2016 in India, and Cowley et al., 2018; Joss et al., 2017, 2019 in the UK.)

The proposal documents should also be read carefully, to determine whether they accurately reflect the results of the public participation exercises. Findings also indicate that there is reason to examine the dynamics of the SPV model, especially whether it promotes environmental sustainability, social inclusion, and capacity building at the local level. As implementation of projects under the SCM continues, an analysis of whether the envisioned objectives were met may yield further insights on the efficacy of the consultant-led planning process. The increasing influence of consultants on public programs calls for sustained empirical examination. This is particularly important in developing country contexts, where public resources are stressed, and processes of consultation and public participation are still not uniformly effective.

Supplemental Material

Supplemental Material - Challenges of consultant-led planning in India’s smart cities mission

Supplemental Material for Challenges of consultant-led planning in India’s smart cities mission by Surajit Chakravarty, Mohammed S Bin Mansoor, Bibek Kumar, and Priya Seetharaman in Environment and Planning B: Urban Analytics and City Science.

Footnotes

Acknowledgments

The authors would like to thank Prof. Rahul Roy, Dr Ramasubramanian Sundararajan and Prof. Somwrita Sarkar for their comments on earlier drafts of the paper.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Surajit Chakravarty

Supplemental Material

Supplemental material for this article is available online.

Surajit Chakravarty is a faculty member at the School of Public Policy, Indian Institute of Technology Delhi. He holds a PhD from the Sol Price School, University of Southern California. His research focuses on urban planning, particularly aspects of housing, public space, public transport and applications of technology. He also engages with questions of temporalities, informality and civic engagement, and is interested in the development of theory based on the experiences of the Global South.

Mohammed Sharique Bin Mansoor did his Masters and Bachelors from Indian Institute of Technology Kanpur in Mechanical Engineering and subsequently completed his MBA from Indian Institute of Management Calcutta. He has worked in diverse fields of Automotive and Energy domain and is currently pursuing a career as Strategy consultant with Accenture. His interests lie in the field of digital transformation, cloud computing and applied intelligence.

Bibek Kumar graduated from Indian Institute of Technology, Kanpur in Electrical Engineering and subsequently completed his MBA from Indian Institute of Management, Calcutta. He has worked in the financial services industry primarily in asset management. His interests are in applications of quantitative analysis, particularly in financial modeling. He is also interested in the analysis of the effectiveness of different technology-enabled policy initiatives in developing country contexts.

Priya Seetharaman is a faculty member at Indian Institute of Management Calcutta. Her primary research interests are in the areas of information technology adoption, use and impacts on organizations and society; IT governance and strategy; digital transformation and e-government. Her research has appeared in academic journals such as Journal of Management Information Systems, Information & Management, Technology Forecasting and Social Change, International Journal of Information Management, Computers in Human Behavior; practitioner journals such as IT Professional apart from various information systems conference proceedings.

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