The increasing evidence base and public concern on the health effects of exposure to high levels of air pollution, combined with stricter environmental legislation, are forcing local governments to take drastic measures. One of the policy instruments, the low emission zone (LEZ), specifically targets a reduction in emissions from vehicles, a key source in urban environments. It is a contested instrument, with supporters who think it is a fair “polluter pays” instrument that especially benefits more deprived communities, while opponents fear an unequal social impact on people's accessibility and finances. This study wants to add a data-driven perspective to the discussion by simultaneously analysing the unequal exposure to air pollution and the unequal accessibility impact, in a comparative study of the LEZs in London and Brussels. The analysis combines a conventional multivariate regression analysis with a geographically weighted regression (GWR) modelling to define the local spatial variation in the relationships, which is of particular concern when considering an explicitly spatial problem and solution. The study shows that GWR is a promising method in distributional environmental justice research through identifying parts of the city where effects are more unequal, as such facilitating customized policy instruments and targeted support.

Background: The petrochemical industry is a major source of hazardous and toxic air pollutants that are recognised to have mutagenic and carcinogenic properties. A wealth of occupational epidemiology literature exists around the petrochemical industry, with adverse haematological effects identified in employees exposed to 'low' concentrations of aromatic hydrocarbons (benzene, toluene, ethylbenzene, and xylene). Releases from the petrochemical industry are also thought to increase the risk of cancer incidence in fenceline communities. However, this emerging and at times inconclusive evidence base remains fragmented. The present study's aim was to conduct a systematic review and meta-analysis of epidemiological studies investigating the association between incidences of haematological malignancy and residential exposure to the petrochemical industry. Methods: Epidemiological studies reporting the risk of haematological malignancies (Leukaemia, Hodgkin's lymphoma, Non-Hodgkin's lymphoma, and Multiple myeloma) were included where the following criteria were met: (i) Cancer incidence is diagnosed by a medical professional and coded in accordance to the International Classification of Diseases; (ii) A clear definition of fenceline communities is provided, indicating the proximity between exposed residents and petrochemical activities; and (iii) Exposure is representative of normal operating conditions, not emergency events. Two investigators independently extracted information on study characteristics and outcomes in accordance with PRISMA and MOOSE guidelines. Relative risks and their 95% confidence intervals were pooled across studies for the four categories of haematological malignancy, using a random effects meta-analysis. Results: The systematic review identified 16 unique studies, which collectively record the incidence of haematological malignancies across 187,585 residents living close to a petrochemical operation. Residents from fenceline communities, less than 5 km from a petrochemical facility (refinery or manufacturer of commercial chemicals), had a 30% higher risk of developing Leukaemia than residents from communities with no petrochemical activity. Meanwhile, the association between exposure and rarer forms of haematological malignancy remains uncertain, with further research required. Conclusions: The risk of developing Leukaemia appears higher in individuals living near a petrochemical facility. This highlights the need for further policy to regulate the release of carcinogens by industry. Graphical abstract: [Figure not available: see fulltext.]

The global petrochemical industry has long been characterized by stable patterns of Western corporate and geographic leadership, but since the early 2000s, the global playing field has changed significantly. China has overtaken the US and Europe as the world’s largest petrochemical producer, and other emerging economies have become global petrochemical players. Combining insights from scholarship on global corporate elites, world city networks, and relational economic geography, this article examines patterns in the corporate networks of leading petrochemical corporations. The research is based on a multiscalar corporate network analysis, applying social network analysis to identify board interlocks, joint venture interlocks, and spatial interlocks between corporations. Through analyzing corporate networks across multiple scales, the research reveals patterns of both integration and isolation within the petrochemical industry. Isolation is evident in disconnected regional corporate elite networks, where the established North Atlantic corporate elite is interconnected through board interlocks, while corporate networks in Asia and other emerging economies remain disconnected. However, high levels of integration within the industry are also evident in an interconnected international company system formed through joint venture collaborations and in overlapping subsidiary networks centered on petrochemical hubs around the world. The article argues that the results demonstrate a combination of resilience and change, or path dependence and contingency, in patterns of corporate power and collaboration. Western company networks still form the social and spatial backbone of the industry, but these have been challenged by emerging strategic centers and isolated elite networks in other parts of the world. This article contributes to debates on industrial corporate elites, multiple globalizations, and the multipolar global economy.

Following the growing empirical evidence on the health effects of air pollution and noise, the fair distribution of these impacts receives increasing attention. The existing environmental inequality studies often focus on a single environmental impact, apply a limited range of covariates or do not correct for spatial autocorrelation. This article presents a geospatial data analysis on Ghent (Belgium), combining residential exposure to air pollution and noise with socioeconomic variables and housing variables. The global results show that neighborhoods with lower household incomes, more unemployment, more people of foreign origin, more rental houses, and higher residential mobility, are more exposed to air pollution, but not to noise. Multiple regression models to explain exposure to air pollution show that residential mobility and percentage of rental houses are the strongest predictors, stressing the role of the housing market in explaining which people are most at risk. Applying spatial regression models leads to better models but reduces the importance of all covariates, leaving income and residential mobility as the only significant predictors for air pollution exposure. While traditional multiple regression models were not significant for explaining noise exposure, spatial regression models were, and also indicate the significant contribution of income to the model. This means income is a robust predictor for both air pollution and noise exposure across the whole urban territory. The results provide a good starting point for discussions about environmental justice and the need for policy action. The study also underlines the importance of taking spatial autocorrelation into account when analyzing environmental inequality.

A growing empirical evidence base identifies environmental noise exposure as an important health problem. While the health effects depend for a great part on personal noise sensitivity and contextual factors, in environmental policy, generic noise standards and procedures based on objective sound levels are used. In this article, the relation between objective and subjective noise exposure variables is further explored by carrying out a residents’ survey in a highly noise polluted area along two highways south of the city centre of Ghent, Belgium. The survey results show only a weak correlation between objective and subjective exposure variables, with both variables demonstrating different associations with the respondents’ background characteristics. While lower-educated and lower-income people are generally higher exposed according to the models, they do not report a higher subjective exposure. People who have been living longer in the area are not necessarily higher exposed according to the models but do report a higher subjective exposure. Most strikingly, owners of a comfortable detached house report a higher subjective exposure than renters of an apartment or small house, while the latter group is significantly higher exposed according to the models. The results support a plea for the joint evaluation of both objective and subjective noise exposure variables in environmental policy and environmental assessments. In addition, the results argue for specific attention for noise exposure of socio-economically vulnerable people and the establishment of a shared knowledge base on noise exposure with both objective and subjective information.

The open space in Flanders, the northern part of Belgium, can hardly be seen as really open. From the Middle Ages onward this area has been known for its spread out development pattern, which has even strengthened in recent decades. Especially the residential ribbon development and the omnipresent infrastructure are widely recognised. These developments have led to an intense fragmentation of open space. In this paper we present two new methods to analyse and quantify this fragmentation of open space from a spatial planning perspective, and compare them with two existing methods. This comparative analysis evaluates the different methods and connects them to different definitions of fragmentation. The average patch size method is more appropriate to describe general fragmentation if the focus is on major line infrastructures, whereas the density of fragmenting structures method matches with the interpretation of fragmentation as spatial heterogeneity. The two described methods to detect enclosed open space fragments as signs of fragmentation give different results depending on the data and methods used. The ribbon method however is more appropriate to detect open space fragments under threat of privatisation, since it works with a stricter definition of continuous ribbon development. All four methods are relevant for Flemish spatial planning policy, as they indicate where actions are needed to safeguard open space from further urbanisation tendencies. Furthermore, they can support a differentiated spatial policy and add to the scientific basis of the debate on alternative interpretations of Flemish open space.