Circular economy and the city: an urban political economy agenda

ABSTRACT

The circular economy (CE) has become a matter of urban development. A literature review shows that the CE debate is biased toward technology-driven industrial change, while bracketing broader socio-political interests. We address this gap by exploring the political economy of scale of the CE. Looking into the case of Brussels (Belgium), a city that has recently adopted the CE as part of its socio-economic strategy, we explore how the anticipated transition to a ‘circular city’ chimes with long-standing urban development agendas. While there is little evidence of stable growth coalitions between corporate and political elites, we argue that the CE provides an ‘urban sustainability fix’ by selectively incorporating ecological goals in urban governance strategies. We further scrutinise the landscape of diverse and heterogenous CE practices in food and transport, highlighting how they are regulated and organised, what labour conditions they offer, and how they are anchored in urban space.     

Chinese in France: diasporic experiences vary depending on generation and age of arrival

ABSTRACT

The objective of this study is to identify the factors that contribute to the successful or unsuccessful integration of the Chinese diaspora in France. There is already a significant amount of literature that probes into the immigrant experiences of various other groups in France. However, the Chinese diaspora in France has remained insufficiently studied. This study aims to address this gap. The main findings of this study are that 1) unsurprisingly, the older the age of arrival, the less well integrated immigrants are in French society, 2) somewhat surprisingly, the less well integrated an immigrant is, the more likely he or she is going to believe that France is a tolerant and open-minded country, and 3) counterintuitively, the more deeply a member of the Chinese diaspora is immersed in French society, the more likely he or she is going to believe that France is hostile to people of Asian heritage.     

Bayesian analysis of multivariate survival data using Monte Carlo methods

This paper deals with the analysis of multivariate survival data from a Bayesian perspective using Markov-chain Monte Carlo methods. The Metropolis along with the Gibbs algorithm is used to calculate some of the marginal posterior distributions. A multivariate survival model is proposed, since survival times within the same group are correlated as a consequence of a frailty random block effect. The conditional proportional-hazards model of Clayton and Cuzick is used with a martingale structured prior process (Arjas and Gasbarra) for the discretized baseline hazard. Besides the calculation of the marginal posterior distributions of the parameters of interest, this paper presents some Bayesian EDA diagnostic techniques to detect model adequacy. The methodology is exemplified with kidney infection data where the times to infections within the same patients are expected to be correlated.     

Bayesian estimation of the linear regression model with an uncertain interval constraint on coefficients

This article considers Bayesian inference in the interval constrained normal linear regression model. Whereas much of the previous literature has concentrated on the case where the prior constraint is correctly specified, our framework explicitly allows for the possibility of an invalid constraint. We adopt a non-informative prior and uncertainty concerning the interval restriction is represented by two prior odds ratios. The sampling theoretic risk of the resulting Bayesian interval pre-test estimator is derived, illustrated and explored. The authors are grateful to the editor and the referee for their helpful comments.     

Bayesian MARS

A Bayesian approach to multivariate adaptive regression spline (MARS) fitting (Friedman, 1991) is proposed. This takes the form of a probability distribution over the space of possible MARS models which is explored using reversible jump Markov chain Monte Carlo methods (Green, 1995). The generated sample of MARS models produced is shown to have good predictive power when averaged and allows easy interpretation of the relative importance of predictors to the overall fit.     

A Study of Interval Censoring in Parametric Regression Models

Parametric models for interval censored data can now easily be fitted with minimal programming in certain standard statistical software packages. Regression equations can be introduced, both for the location and for the dispersion parameters. Finite mixture models can also be fitted, with a point mass on right (or left) censored observations, to allow for individuals who cannot have the event (or already have it). This mixing probability can also be allowed to follow a regression equation.Here, models based on nine different distributions are compared for three examples of heavily censored data as well as a set of simulated data. We find that, for parametric models, interval censoring can often be ignored and that the density, at centres of intervals, can be used instead in the likelihood function, although the approximation is not always reliable. In the context of heavily interval censored data, the conclusions from parametric models are remarkably robust with changing distributional assumptions and generally more informative than the corresponding non-parametric models.