Supervision behaviours of customs supervisors on solid waste import in Shanghai, People's Republic of China.

Waste management is a key challenge the world currently faces. Solid waste imports (SWIs) are counteractive to the construction of ecological civilization. The comprehensive prohibition of "foreign waste" (FW) imports (FWIs) is an iconic measure to promote ecological civilization and ecological environment safety. Strengthening the People's Republic of China (PRC)'s supervision of SWI (SSWIs) is an important means of implementing the comprehensive prohibition of FWIs, while existing research has paid little attention to the behaviour of SSWIs. This paper firstly analyses the influential factors of the behaviour of SSWIs based on the theory of planned behaviour (TPB), and five corresponding research hypotheses are put forward accordingly. Then, it uses 262 micro questionnaires regarding the behaviour of SSWIs in the Port of Waigaoqiao in Shanghai, PRC, and constructs a structural equation model based on the TPB to identify the influential factors and effects of the behaviour of SSWIs. Some interesting findings are observed in this research. Firstly, it indicates that attitude, subjective norms (SNs) and perceived behavioural control (PBC), all indirectly act upon supervision behaviour by influencing supervision intention. Secondly, PBC acts directly on the behaviour of SSWIs, which is consistent with the TPB. Thirdly, the direct effects on supervision intention from strongest to weakest are SNs, attitude, and PBC, respectively. The conclusion provides important policy enlightenment for the supervision optimization of the behaviour of SSWIs. The supervision intention can be motivated from various angles by strengthening the attitude, SNs and PBC of customs supervisors, subsequently strengthening their supervision intention and behaviour.

Real time analysis of lead-containing atmospheric particles in Guangzhou during wintertime using single particle aerosol mass spectrometry.

The toxic effects of lead on human health and the environment have long been a focus of research. To explore sources of lead in Guangzhou, China, we investigated atmospheric lead-containing particles (LCPs) during wintertime using a single particle aerosol mass spectrometer (SPAMS). Based on mass spectral features, LCPs were classified into eight major particle types, including Pb-Cl and Pb-Cl-Li (coal combustion and waste incineration), Pb-Cl-EC and Pb-Cl-OC (diesel trucks and coal combustion), Pb-Cl-Fe (iron and steel industry), Pb-Cl-AlSi (dust), Pb-Sec (secondary formation), and Pb-Cl-Zn (industrial process); these sources (in parentheses) were identified by comparing atmospheric LCP mass spectra with authentic Pb emission source mass spectra. Sampling periods with LCP number fractions (NFs) more than three times the average LCP NF (APF = 4.35%) and below the APF were defined as high LCP NF periods (HLFPs: H1, H3, and H5) and low LCP NF APF periods (LLFPs: L2 and L4), respectively. Diurnal patterns and high Pb-Sec content during LLFPs indicate that photochemical activity and heterogeneous reactions may have controlled Pb-Sec particle formation. The inverse Pb-Cl and Pb-Sec particle diurnal trends during LLFPs suggest the replacement of Cl by sulfate and nitrate. On average over the five periods, ~ 76% of the LCPs likely arose from coal combustion and/or waste incineration, which were dominant sources during all five periods, followed by diesel trucks during LLFPs and iron- and steel-related sources during HLFPs; HLFP LCPs arose mainly from primary emissions. These results can be used to more efficiently control Pb emission sources and prevent harm to human and environmental health from Pb toxicity.

Asymmetric Transfer Hashing With Adaptive Bipartite Graph Learning.

Thanks to the efficient retrieval speed and low storage consumption, learning to hash has been widely used in visual retrieval tasks. However, the known hashing methods assume that the query and retrieval samples lie in homogeneous feature space within the same domain. As a result, they cannot be directly applied to heterogeneous cross-domain retrieval. In this article, we propose a generalized image transfer retrieval (GITR) problem, which encounters two crucial bottlenecks: 1) the query and retrieval samples may come from different domains, leading to an inevitable domain distribution gap and 2) the features of the two domains may be heterogeneous or misaligned, bringing up an additional feature gap. To address the GITR problem, we propose an asymmetric transfer hashing (ATH) framework with its unsupervised/semisupervised/supervised realizations. Specifically, ATH characterizes the domain distribution gap by the discrepancy between two asymmetric hash functions, and minimizes the feature gap with the help of a novel adaptive bipartite graph constructed on cross-domain data. By jointly optimizing asymmetric hash functions and the bipartite graph, not only can knowledge transfer be achieved but information loss caused by feature alignment can also be avoided. Meanwhile, to alleviate negative transfer, the intrinsic geometrical structure of single-domain data is preserved by involving a domain affinity graph. Extensive experiments on both single-domain and cross-domain benchmarks under different GITR subtasks indicate the superiority of our ATH method in comparison with the state-of-the-art hashing methods.

How Does Spatial Injustice Affect Residents' Policy Acceptance of the Economic-Social-Ecological Objectives of Construction Land Reduction?

Construction land reduction (CLR) is a policy innovation for Shanghai to explore high-quality economic development, but it will also lead to spatial injustice in the implementation process. Although the literature on spatial injustice and CLR is increasing, very little is known about the influence of spatial injustice in CLR on residents' policy acceptance of the economic-social-ecological objectives of CLR. To fill the knowledge gap, this study uses micro-survey data to identify the factors that influence residents' policy acceptance of the economic-social-ecological objectives of CLR. Results show that: (1) Spatial injustice in CLR significantly reduces residents' policy acceptance of the social and ecological objectives of CLR. (2) The locational disadvantage of villages significantly reduces residents' policy acceptance of the ecological objectives of CLR. (3) The more educated the residents are, the more they recognize the social and ecological objectives of CLR. (4) The higher the percentage of household workers, the more residents endorse the economic and social objectives of CLR. (5) Compared with ordinary residents, cadres are more accepting of the economic objectives of CLR. (6) Robustness tests support the findings of this study. The findings of this study provide insights for sustainable CLR policy reform.

Land Development Rights, Spatial Injustice, and the Economic Development in Net-Incremental Reduction Regions of Construction Land: Evidence from Shanghai, China.

Rapid urbanization raises the issue of protecting development interests in net-incremental reduction regions of construction land (NRRCL). Spatial injustice (SI) is one of the key factors for the smooth implementation of construction land reduction (CLR) policies. This study theoretically analyzes the influence of SI on the economic development in CLR saving quota outflow regions, namely, NRRCL, and conducted empirical tests with the difference-in-differences model. The findings reveal that: (1) regional differences in CLR policy promote the transfer of land development rights from NRRCL to net-incremental increase regions of construction land (NIRCL) in economically developed regions, thus resulting in SI; (2) SI limits the economic development of NRRCL; (3) land-use planning negatively impacts economic development in planning reduced-type regions; (4) the off-site realization of spatial justice in the CLR process in suburbs has comparative advantages; (5) in the process of CLR, it is vital to promote the transfer of population from NRRCL to NIRCL to alleviate the negative impact of SI.

Generalized Embedding Regression: A Framework for Supervised Feature Extraction.

Sparse discriminative projection learning has attracted much attention due to its good performance in recognition tasks. In this article, a framework called generalized embedding regression (GER) is proposed, which can simultaneously perform low-dimensional embedding and sparse projection learning in a joint objective function with a generalized orthogonal constraint. Moreover, the label information is integrated into the model to preserve the global structure of data, and a rank constraint is imposed on the regression matrix to explore the underlying correlation structure of classes. Theoretical analysis shows that GER can obtain the same or approximate solution as some related methods with special settings. By utilizing this framework as a general platform, we design a novel supervised feature extraction approach called jointly sparse embedding regression (JSER). In JSER, we construct an intrinsic graph to characterize the intraclass similarity and a penalty graph to indicate the interclass separability. Then, the penalty graph Laplacian is used as the constraint matrix in the generalized orthogonal constraint to deal with interclass marginal points. Moreover, the L2,1 -norm is imposed on the regression terms for robustness to outliers and data's variations and the regularization term for jointly sparse projection learning, leading to interesting semantic interpretability. An effective iterative algorithm is elaborately designed to solve the optimization problem of JSER. Theoretically, we prove that the subproblem of JSER is essentially an unbalanced Procrustes problem and can be solved iteratively. The convergence of the designed algorithm is also proved. Experimental results on six well-known data sets indicate the competitive performance and latent properties of JSER.

Influential factors affecting the generation of kitchen solid waste in Shanghai, China.

The influential factors of the generation of kitchen solid waste (KSW) are vital to design policies that target effective KSW reductions and achieve sustainable KSW management, but has not been extensively studied. The study aims at the first step of KSW management only, i.e., generation, which is the first KSW analysis survey in Shanghai to comprehensively investigate the characteristics and influential factors of KSW generation (KSWG). The data were collected from August to October 2019, which resulted in a total of 433 usable questionnaires regarding KSWG in Shanghai, China. The average rate of KSWG is 6.038 kg/household/week or 0.265 kg/person/day. The average per-person number of KSWG bags is 0.295. The regression results show that the characteristics of KSWG are related to household size, household income, the habit of ordering takeout, district of residence, and meticulous consumption culture. Some interesting findings are observed in the present study. Firstly, there is a nonlinear relationship between KSWG and household size, which can be explained by "common consumption". Secondly, there is a nonlinear relationship between KSWG and household income, which can be explained by the Engel coefficient, which also provides supporting evidence for the environmental Kuznets curve (EKC) hypothesis. Finally, the habit of ordering takeout, district of residence, and meticulous consumption culture are all influential factors of KSWG. The results of the study can improve public awareness and knowledge of KSWG, and suggestions on the minimization of KSWG are put forward, thus will serve as a new reference for the sustainable KSW management of other cities, especially cities in developing countries.Implications: In the practice of sustainable waste management, KSWG is a complex issue that requires a broad analytic approach that considers several factors simultaneously. From a practical perspective, policy decision-makers should pay attention to at least four important actions to reduce KSWG: Firstly, compared with urban areas, rural areas are important areas for reducing KSWG, which should be given sufficient attention. Secondly, compared with low-income households, high-income households are the focus of KSWG reduction and should be considered. Thirdly, meticulous consumption culture plays an important role in promoting the reduction of KSWG, and this culture should be promoted and cultivated in the formulation of actual waste management policies. This is also an important way to reduce KSWG and should be fully considered. Finally, in the pilot process of domestic waste separation in Chinese cities, 3 R (Reduce, Reuse, and Recycle) waste management concept and method in circular economy can be applied to KSW treatment, and adequate financial support is necessary. Through strengthening the management of KSW, the potential value of KSW can be further developed, so as to realize KSWG reduction and resource utilization.