p Nas décadas recentes, as populações urbanas nas cidades chinesas cresceram substancialmente, e o aumento da renda levou a uma rápida expansão da propriedade de automóveis. De fato, A China é hoje o maior mercado mundial de automóveis. A combinação de urbanização e motorização levou a uma necessidade urgente de políticas de transporte para resolver problemas urbanos, como congestionamento, poluição do ar, e emissões de gases de efeito estufa. p Nos últimos três anos, uma equipe do MIT liderada por Joanna Moody, gerente do programa de pesquisa do Centro de Sistemas de Mobilidade da MIT Energy Initiative, e Jinhua Zhao, o professor associado Edward H. e Joyce Linde no Departamento de Estudos e Planejamento Urbano (DUSP) e diretor do Laboratório de Mobilidade Urbana JTL do MIT, tem examinado a política de transporte e a formulação de políticas na China. "É frequentemente assumido que a política de transporte na China é ditada pelo governo nacional, "diz Zhao." Mas vimos que o governo nacional estabelece metas e então permite que cidades individuais decidam quais políticas implementar para atingir essas metas. "

p Muitos estudos investigaram a formulação de políticas de transporte em megacidades da China, como Pequim e Xangai, mas poucos se concentraram nas centenas de cidades de pequeno e médio porte localizadas em todo o país. Tão Moody, Zhao, e sua equipe queria considerar o processo nessas cidades negligenciadas. Em particular, eles perguntaram:como os líderes municipais decidem quais políticas de transporte implementar, e eles podem aprender melhor com as experiências uns dos outros? As respostas a essas perguntas podem fornecer orientação aos tomadores de decisão municipais que tentam abordar os diferentes desafios relacionados ao transporte enfrentados por suas cidades.

p As respostas também podem ajudar a preencher uma lacuna na literatura de pesquisa. O número e a diversidade de cidades em toda a China tornaram a realização de um estudo sistemático da política de transporte urbano um desafio, no entanto, esse tópico é cada vez mais importante. Em resposta à poluição do ar local e congestionamento de tráfego, algumas cidades chinesas estão adotando políticas para restringir a propriedade e o uso de automóveis, e essas políticas locais podem, em última instância, determinar se o crescimento sem precedentes nas vendas de veículos particulares em todo o país persistirá nas próximas décadas.

p Aprendizagem de políticas

p Os formuladores de políticas de transporte em todo o mundo se beneficiam de uma prática chamada aprendizagem de políticas:os tomadores de decisão em uma cidade olham para outras cidades para ver quais políticas foram ou não eficazes. Na China, Pequim e Xangai são geralmente vistos como formadores de opinião na formulação de políticas de transporte inovadoras, e os líderes municipais em outras cidades chinesas recorrem a essas megacidades como modelos de comportamento.

p Mas essa é uma abordagem eficaz para eles? Afinal, suas configurações urbanas e desafios de transporte são quase certamente bem diferentes. Não seria melhor se eles procurassem "pares" de cidades com as quais têm mais em comum?

p Temperamental, Zhao, e seus colegas DUSP - pós-doutorado Shenhao Wang e alunos de pós-graduação Jungwoo Chun e Xuenan Ni, tudo no Laboratório de Mobilidade Urbana da JTL - hipótese de uma estrutura alternativa para o aprendizado de políticas em que as cidades que compartilham histórias comuns de urbanização e motorização compartilhariam seus conhecimentos sobre políticas. O desenvolvimento semelhante de espaços urbanos e padrões de viagens pode levar aos mesmos desafios de transporte, e, portanto, para necessidades semelhantes de políticas de transporte.

p Para testar sua hipótese, os pesquisadores precisavam responder a duas questões. Para iniciar, eles precisavam saber se as cidades chinesas têm um número limitado de histórias comuns de urbanização e motorização. Se eles agrupassem as 287 cidades da China com base nessas histórias, eles acabariam com um número moderadamente pequeno de grupos significativos de cidades semelhantes? E em segundo lugar, as cidades em cada grupo teriam políticas e prioridades de transporte semelhantes?

p Agrupando as cidades

p As cidades na China costumam ser agrupadas em três "níveis" com base na administração política, ou os tipos de papéis jurisdicionais que as cidades desempenham. O nível 1 inclui Pequim, Xangai, e duas outras cidades que têm os mesmos poderes políticos das províncias. O Nível 2 inclui cerca de 20 capitais de província. The remaining cities—some 260 of them—all fall into Tier 3. These groupings are not necessarily relevant to the cities' local urban and transportation conditions.

p Moody, Zhao, and their colleagues instead wanted to sort the 287 cities based on their urbanization and motorization histories. Felizmente, they had relatively easy access to the data they needed. Every year, the Chinese government requires each city to report well-defined statistics on a variety of measures and to make them public.

p Among those measures, the researchers chose four indicators of urbanization—gross domestic product per capita, total urban population, urban population density, and road area per capita—and four indicators of motorization—the number of automobiles, taxis, buses, and subway lines per capita. They compiled those data from 2001 to 2014 for each of the 287 cities.

p The next step was to sort the cities into groups based on those historical datasets—a task they accomplished using a clustering algorithm. For the algorithm to work well, they needed to select parameters that would summarize trends in the time series data for each indicator in each city. They found that they could summarize the 14-year change in each indicator using the mean value and two additional variables:the slope of change over time and the rate at which the slope changes (the acceleration).

p Based on those data, the clustering algorithm examined different possible numbers of groupings, and four gave the best outcome in terms of the cities' urbanization and motorization histories. "With four groups, the cities were most similar within each cluster and most different across the clusters, " says Moody. "Adding more groups gave no additional benefit."

p The four groups of similar cities are as follows:

• Cluster 1:23 large, dense, wealthy megacities that have urban rail systems and high overall mobility levels over all modes, including buses, taxis, and private cars. This cluster encompasses most of the government's Tier 1 and Tier 2 cities, while the Tier 3 cities are distributed among Clusters 2, 3, e 4.
• Cluster 2:41 wealthy cities that don't have urban rail and therefore are more sprawling, have lower population density, and have auto-oriented travel patterns.
• Cluster 3:134 medium-wealth cities that have a low-density urban form and moderate mobility fairly spread across different modes, with limited but emerging car use.
• Cluster 4:89 low-income cities that have generally lower levels of mobility, with some public transit buses but not many roads. Because people usually walk, these cities are concentrated in terms of density and development.

p City clusters and policy priorities

p The researchers' next task was to determine whether the cities within a given cluster have transportation policy priorities that are similar to each other—and also different from those of cities in the other clusters. With no quantitative data to analyze, the researchers needed to look for such patterns using a different approach.

p Primeiro, they selected 44 cities at random (with the stipulation that at least 10 percent of the cities in each cluster had to be represented). They then downloaded the 2017 mayoral report from each of the 44 cities.

p Those reports highlight the main policy initiatives and directions of the city in the past year, so they include all types of policymaking. To identify the transportation-oriented sections of the reports, the researchers performed keyword searches on terms such as transportation, estrada, carro, bus, and public transit. They extracted any sections highlighting transportation initiatives and manually labeled each of the text segments with one of 21 policy types. They then created a spreadsheet organizing the cities into the four clusters. Finalmente, they examined the outcome to see whether there were clear patterns within and across clusters in terms of the types of policies they prioritize.

p "We found strikingly clear patterns in the types of transportation policies adopted within city clusters and clear differences across clusters, " says Moody. "That reinforced our hypothesis that different motorization and urbanization trajectories would be reflected in very different policy priorities."

p Here are some highlights of the policy priorities within the clusters:

p The cities in Cluster 1 have urban rail systems and are starting to consider policies around them. Por exemplo, how can they better connect their rail systems with other transportation modes—for instance, by taking steps to integrate them with buses or with walking infrastructure? How can they plan their land use and urban development to be more transit-oriented, such as by providing mixed-use development around the existing rail network?

p Cluster 2 cities are building urban rail systems, but they're generally not yet thinking about other policies that can come with rail development. They could learn from Cluster 1 cities about other factors to take into account at the outset. Por exemplo, they could develop their urban rail with issues of multi-modality and of transit-oriented development in mind.

p In Cluster 3 cities, policies tend to emphasize electrifying buses and providing improved and expanded bus service. In these cities with no rail networks, the focus is on making buses work better.

p Cluster 4 cities are still focused on road development, even within their urban areas. Policy priorities often emphasize connecting the urban core to rural areas and to adjacent cities—steps that will give their populations access to the region as a whole, expanding the opportunities available to them.

p Benefits of a "mixed method" approach

p Results of the researchers' analysis thus support their initial hypothesis. "Different urbanization and motorization trends that we captured in the clustering analysis are reflective of very different transportation priorities, " says Moody. "That match means we can use this approach for further policymaking analysis."

p At the outset, she viewed their study as a "proof of concept" for performing transportation policy studies using a mixed-method approach. Mixed-method research involves a blending of quantitative and qualitative approaches. In their case, the former was the mathematical analysis of time series data, and the latter was the in-depth review of city government reports to identify transportation policy priorities. "Mixed-method research is a growing area of interest, and it's a powerful and valuable tool, " says Moody.

p She did, Contudo, find the experience of combining the quantitative and qualitative work challenging. "There weren't many examples of people doing something similar, and that meant that we had to make sure that our quantitative work was defensible, that our qualitative work was defensible, and that the combination of them was defensible and meaningful, " ela diz.

p The results of their work confirm that their novel analytical framework could be used in other large, rapidly developing countries with heterogeneous urban areas. "It's probable that if you were to do this type of analysis for cities in, dizer, Índia, you might get a different number of city types, and those city types could be very different from what we got in China, " says Moody. Regardless of the setting, the capabilities provided by this kind of mixed method framework should prove increasingly important as more and more cities around the world begin innovating and learning from one another how to shape sustainable urban transportation systems. p Esta história foi republicada por cortesia do MIT News (web.mit.edu/newsoffice/), um site popular que cobre notícias sobre pesquisas do MIT, inovação e ensino.