Qiang Cao (p)

Henri de Groot

While innovation in the periphery has attracted increasing scholarly attention, the mechanisms driving innovation and territorial positional transformation remain largely under-investigated. Theoretically, we adopt a dual core-periphery framework to analyze how both geographical and relational positions shape innovation and positional transformation. We integrate the differentiated knowledge bases approach to examine the relationship between positions and innovation through knowledge processes. Empirically, we examine a Chinese agricultural region that has transitioned from a network periphery to a core position within the national vegetable industry, accumulating substantial synthetic knowledge. Recently, seed breeding firms have emerged as new innovative actors, relying on analytical knowledge for their innovation processes. Focusing on the processes and mechanisms of positional transformation and their influence on new innovative actors, we find that Shouguang’s transformation is driven by its territorial innovation system. New innovative actors benefit from their network core position while remaining geographically peripheral, as local social networks facilitate their integration into territorial innovation system, while external networks support their search for analytical knowledge. However, this transformation has also led to environmental and social challenges that are often overlooked due to its peripheral geographical status. This study contributes to economic geography literature by revealing the dynamics, mechanisms, and impacts of innovation in the periphery through a knowledge and evolutionary perspective, considering both geographical and relational positions.

Erkinai Derkenbaeva (p)

Henri de Groot

As urban areas face increasing energy demands and the challenges of climate change, the adoption of photovoltaic (PV) systems is crucial for enhancing energy security and promoting sustainable development. This study investigates the spatial distribution and temporal trends of PV adoption in Amsterdam from 2016 to 2023, with a focus on various building types, including residential and commercial structures. Using geographic information system (GIS) data, the research maps PV installation locations and conducts spatial autocorrelation analysis to identify hotspots of adoption. Regression analyses explore the relationship between building characteristics (e.g., age and type) and PV uptake, as well as the influence of household demographics on residential PV adoption. The findings reveal distinct spatial patterns, with high adoption concentrations in specific neighborhoods, often linked to favorable socio-economic and infrastructural conditions. Conversely, other areas exhibit lower adoption rates, indicating barriers related to urban density, building constraints, and lagging uptake in multi-unit dwellings. Additionally, demographic factors, including the prevalence of younger populations, single-person households, and families without children, are identified as critical factors influencing PV adoption. The study's outcomes highlight the need for targeted interventions to overcome adoption barriers and ensure equitable access to sustainable energy solutions. Ultimately, the research aims to provide actionable insights for policymakers and urban planners to design policies that support the widespread adoption of PV systems in Amsterdam, fostering a more sustainable urban energy future.

Conor O'Driscoll (p)

Henri de Groot

Classical theories in urban economics and regional science posit that individuals achieve a spatial equilibrium by balancing commuting costs with land rents, resulting in stable residential and travel behaviours. However, this equilibrium is frequently disrupted by life-course events, offering valuable opportunities to study behavioural adaptation. This paper investigates how residential relocations causally affect commute mode switching, shedding light on the relative influence of local spatial structures versus internal life dynamics. I use individual-level panel data, incorporating detailed information on relocations, commuting, and highly granular neighbourhood characteristics. A pre-treatment event study regression design is used to isolate the causal effect of moving on commute mode choice. Results show a marked increase in the probability of switching commute mode in the year of relocation. Heterogeneity analyses further reveal that responses vary by motivation for moving and spatial context, suggesting differentiated behavioural flexibility across groups and space. Overall, the findings demonstrate that commuting behaviours are not entirely sticky and that relocation presents a critical window for behavioural change, offering new insights for transport policy, spatial planning, and theories of travel behaviour adaptation.