2.1. Sample

To measure the possible connection between MPO-City alignment and vehicle trip reduction across our four categories, including Transportation and Land Use (TLU) strategies, which look at the four alignments of—(1) transportation infrastructure (built environment), (2) land use policies, (3) Transportation Demand Management (TDM) policies, and (4) cross-cutting issues—we analyzed data from content reviews of CAPs and SCSs to calculate the variable that indicates the alignment between MPO and City. Each category includes a series of strategies, as explained in the Analytical Approach section. For instance, climate-friendly infrastructure, which is part of Category 1, integrates sustainable practices in Transportation and Land Use to reduce greenhouse gas emissions and energy consumption. It includes efficient public transit, cycling and walking infrastructure, and support for electric vehicles. Transit-Oriented Development (TOD) promotes high-density, mixed-use communities near transit hubs, reducing car dependency. Compact urban design, green spaces, and low-carbon construction enhance sustainability. Smart mobility systems and policies like carbon pricing and subsidies incentivize greener choices. Equity is central to ensuring access to sustainable transportation for all. These strategies create efficient, inclusive, and low-carbon cities that mitigate climate impacts and improve the overall quality of life. We then applied multiple linear regression models across the 20 selected cities, with the block group as our unit of analysis. The dataset included 6513 census block groups, adjusted after model refinements. Block groups were chosen as the unit of analysis because they represent the finest available data granularity, allowing us to maximize the observation counts and thereby enhance our models’ statistical strength.

2.2. Data and Variables

We categorized our variables into three main groups. The first group includes built environment features that are recognized as key influences on commuting behavior. These features were sourced from the Environmental Protection Agency’s (EPA) Smart Location Database (SLD). Our analysis considers the four primary built environment factors, often referred to as the “4 Ds”: activity density (the combined numbers of residents and employment divided by area in sq. mile), land-use diversity (measured by the variety of job types), street design (represented by intersection density), and proximity to public transit (approximated by the frequency of transit services). The second category focuses on demographic variables that can significantly impact commuting patterns. This category includes the total population, the percentage of working-age residents, education levels, and age distribution. Additionally, two other critical variables influencing commuting behavior are workplace location and residents’ dependence on automobiles. To assess these, we measured the percentage of commuters living within a 30 min distance from their job and the proportion of those not using cars for their commute in 2010.

2.3. Analytical Approach

To assess the effectiveness of both MPO-City alignment and independent city-level strategies, two binary (dummy) variables were employed. One dummy variable was coded as “1” if both the city and MPO included a specific strategy in their plans and “0” otherwise. The second dummy variable was coded as “1” if the city (regardless of its MPO) included a specific strategy in its plans and “0” otherwise. These dummy variables were then integrated into a series of statistical models to estimate the impact of MPO-City alignment on vehicle trip reduction and compare it with the impact of the city’s action independent of alignment with its MPO. This study used a linear regression modeling approach to quantify the vehicle trip reduction impacts of city-MPO alignment and city action across 20 cities. The analysis was conducted at the census block group (BG) level using a sample of 6513 block groups. Census block groups, which are subdivisions of census tracts containing between 600 and 3000 people, were chosen because they represent the most granular level of data available. This granularity allowed for a higher number of observations, increasing the statistical power of the models. The regression models were refined through iterative modifications to ensure robustness and validity. This involved testing for multicollinearity, heteroscedasticity, and model specification errors. The final models produced statistically significant results that quantified the relationship between (1) MPO-City alignment and vehicle trip reduction and (2) City’s climate action strategies and vehicle trip reduction. In other words, the linear regression models revealed the extent to which alignment or the city’s adoption of policies in each strategy category contributed to vehicle trip reduction. This quantitative approach provided a robust framework for understanding the relationship between MPO-City policy alignment or a city’s climate policy adoption and transportation outcomes, offering valuable insights for future planning and policymaking efforts aimed at reducing greenhouse gas (GHG) emissions in the transportation sector.

3.1. The Impact of the Alignment on 10-Year Vehicle Trip Reduction

Based on the constant value in our model, we predicted an average 20% increase in non-auto commuting across all block groups in our sample, largely due to climate-focused planning strategies. Among the variables analyzed, two stood out as the most influential in promoting non-auto commute trips: the alignment between cities and MPOs (Metropolitan Planning Organizations) in advancing climate-friendly infrastructure strategies and the percentage of educated residents with university degrees. This synergy between city and MPO efforts, when successful, results in a more than 7% boost in non-auto commutes for the average city block group.

The power of climate-friendly infrastructure goes beyond just cutting greenhouse gas emissions—its ripple effect often enhances the walkability of an area. Take, for example, strategies like planting trees or preserving the urban tree canopy. These actions do more than simply absorb carbon; they create shaded, inviting spaces for walking and connecting people with their environment in a more intimate, pedestrian-friendly way. Imagine streets lined with tall, leafy trees offering a cool respite from the sun, encouraging more people to choose walking or cycling over driving. However, pedestrian-friendly environments are not conducive to safe biking because they are designed to serve only pedestrians.

Economic status also factors heavily into these patterns. Affluent areas, particularly those within a 30 min drive of workplaces, tend to see higher rates of vehicle usage. People in these sprawled, wealthier neighborhoods often have disposable income to afford the convenience and comfort of driving. This poses a challenge to efforts aimed at balancing job locations with housing development, particularly if transit systems remain inadequate to support the shift away from car reliance.

Through this analysis, it becomes clear that while regional and city alignment on climate strategies is crucial for reducing vehicle trips, socioeconomic factors and transit infrastructure remain powerful influencers capable of either accelerating or inhibiting progress.

3.2. Transportation Infrastructure/Built Environment

The model reveals that policies designed to support active transportation have a clear positive impact on reducing vehicle trips, especially those centered around mass transit and complete street initiatives. Complete street strategies focus on improving the infrastructure for pedestrians and cyclists, creating a transportation network that accommodates all modes of travel. This involves enhancing sidewalks, bike paths, and other critical infrastructure to encourage walking and biking. Despite the success of these strategies, [1,2the other policies in this category did not show the same impact on reducing vehicle trips. For the first set of strategies, climate-friendly infrastructure stands out as the area where the alignment between MPOs and cities proves significantly more effective than local actions alone. However, other strategies yielded different outcomes. Specifically, city-led initiatives—independent of MPO alignment—were notably more impactful when it came to goods movement and electric vehicle infrastructure.

3.3. Land-Use Policies

The rationale behind this is clear: preserving open space, farmland, and natural areas helps curb urban sprawl, which in turn reduces the need for long-distance vehicle commutes. This type of preservation acts as a buffer against expanding urban development, promoting shorter and more sustainable travel options. Our models also revealed that the MPO-City alignment on parking regulation strategies significantly reduced vehicle trips from 2010 to 2019. These strategies might include parking fees to discourage single-occupant vehicle use or municipal efforts such as “unbundling” parking costs or reducing/eliminating parking minimums. By making parking more expensive or less available, such policies encourage commuters to shift to other modes of transportation.

Moreover, alignment between MPOs and cities on housing strategies—such as building near activity hubs, improving the balance between jobs and housing, and promoting infill development—also contributed to reducing vehicle trips. However, these strategies had a more modest effect compared to those focused on open-space preservation and parking regulations. While still beneficial, housing-related policies had a lower impact on vehicle trip reductions when compared to environmental and parking-focused efforts.

The second category of variables, where direct comparison of t-values is possible, includes ADU (Accessory Dwelling Unit) programs, parking requirements, and urban forest strategies. In all three cases, our models indicate that a city’s independent actions are more effective than those aligned with MPO strategies. In some instances, the results are even reversed when comparing city-driven initiatives to MPO-City alignment efforts. A striking example is the urban forest strategy. While the alignment between MPOs and cities shows a significant *negative* impact on vehicle trip reduction, a city’s independent efforts in this area show a positive and highly significant effect. This suggests that local actions tailored to the specific needs and circumstances of a city may be more effective in promoting urban forests and their role in reducing vehicle trips, perhaps because local governments can directly implement and manage urban green spaces in ways that better fit their environment. Another major shift appears in ADU programs. Here, the model reveals that MPO-City alignment has a significant negative impact on vehicle trip reduction, while a city’s independent action has no significant effect. This may indicate that regionally coordinated efforts to expand ADU programs might not be as successful in reducing vehicle trips as anticipated, possibly due to local variations in housing markets, land use, or infrastructure that make broad regional strategies less effective in achieving their intended outcomes. Meanwhile, the lack of significant results from cities’ independent ADU actions suggests that these programs may not yet be impactful enough to reduce vehicle trips on their own. These findings highlight that, for certain strategies, local governments acting independently may be more successful than regional coordination in achieving transportation and environmental goals. The variation in effectiveness emphasizes the need for flexible, localized approaches, particularly in areas like urban forestry and housing, where a “one-size-fits-all” regional policy may not be appropriate.

3.4. Transportation Demand Management (TDM) Policies

The purpose of these TDM strategies is to improve the efficiency of the transportation system by encouraging a shift to more sustainable modes, such as public transit, walking, biking, and ride-sharing. The alignment between regional and local actions is crucial for creating a unified approach that supports this transition away from car dependency and encourages alternative travel behaviors. This coordination is essential because the successful implementation of these programs often requires seamless integration of infrastructure and services across multiple jurisdictions. Public transit systems, for example, require coordinated planning and service delivery between cities and regions to ensure comprehensive coverage. Similarly, a well-connected and safe network of pedestrian and cycling infrastructure is necessary to make walking and biking convenient and attractive options for travelers.

For the remaining strategies in this category, local actions were found to be more effective than coordination with MPOs. Specifically, when it came to education and outreach policies or initiatives aimed at reducing car travel, such as promoting telecommuting, the MPO-City alignment failed to significantly reduce vehicle trips. In contrast, the independent actions taken by local governments had a stronger influence on reducing driving. This highlights the potential strength of locally focused outreach and engagement efforts in encouraging behavior change within communities.

3.5. Cross-Cutting Issues

In this final group of strategies—cross-cutting issues—our models did not reveal a strong positive effect from MPO-City coordination. The only strategy that showed a slightly positive, although not significant, impact was equity-related initiatives. Equity has become an increasingly prominent aspect of Climate Action Plans and typically includes a variety of efforts, such as protecting vulnerable communities from climate impacts, advancing racial equity, and ensuring that low-income residents have access to transportation and employment opportunities.

To sum up, the most important insight from this analysis is that MPO-City alignment was only significantly effective for climate-friendly infrastructure policies, while for most other variables, independent city actions proved more impactful. In many cases, alignment either had no notable positive effect or was outperformed by local efforts. For instance, city-led initiatives in goods movement, urban forestry, education, outreach programs, and strategies to reduce driving (including telework) had a significantly stronger impact on reducing vehicle trips, while MPO-City alignment in these areas often had the opposite effect. Additionally, although our models showed that regional and local alignments in parking regulations positively influenced vehicle trip reduction, the impact of independent local actions on parking was even more pronounced. These findings suggest that while regional coordination has its place, particularly for broad climate initiatives, local strategies (e.g., expanding protected bike lanes, enhancing public transit, incentivizing energy-efficient buildings, promoting renewable energy, increasing urban green spaces, supporting waste reduction, and fostering community engagement through education and partnerships) tend to be more successful when addressing specific issues or engaging communities directly. In light of these results, the State of California should prioritize supporting both regional and local efforts to reduce transportation emissions. A balanced approach that values local autonomy and regional collaboration will lead to more effective solutions for curbing vehicle trips and advancing climate goals.