This paper examines how an individual's moral norms and demographic characteristics interact with the standard ‘Theory of Planned Behavior’ predictors (Attitude; Subjective Norms; and Perceived Behavioral Control (PBC)) in explaining the intention to recycle (RI). Our data originate from an empirical research of Greek citizens conducted in Autumn 2013 (N = 293). Through structural equation modeling, we find that PBC is consistently the most important predictor of RI. Moral norms have a larger effect on RI than Attitude while their influence is primarily direct. On the contrary, demographic characteristics were found to be statistically non-significant predictors of RI, similarly to Subjective Norms.

pdf/extending-the-theory-of-planned-behavior-in-the-context-of-2pzvts27pc.pdf

Extending the Theory of Planned Behavior in the context of recycling: The role of moral norms and of demographic predictors

Public participation and city sustainability: Evidence from Urban Garbage Classification in China

The accelerated movement of people towards cities led to the fact that the world’s urban population is now growing by 60-million persons per year. The increased number of cities’ population has a significant impact on the produced volume of household waste, which must be collected and recycled in time. The collection of household waste, especially in downtown areas, has a wide range of challenges; the collection system must be reliable, flexible, cost efficient, and green. Within the frame of this paper, the authors describe the application possibilities of Industry 4.0 technologies in waste collection solutions and the optimization potential in their processes. After a systematic literature review, this paper introduces the waste collection process of downtowns as a cyber-physical system. A mathematical model of this waste collection process is described, which incorporates routing, assignment, and scheduling problems. The objectives of the model are the followings: (1) optimal assignment of waste sources to garbage trucks; (2) scheduling of the waste collection through routing of each garbage truck to minimize the total operation cost, increase reliability while comprehensive environmental indicators that have great impact on public health are to be taken into consideration. Next, a binary bat algorithm is described, whose performance is validated with different benchmark functions. The scenario analysis validates the model and then evaluates its performance to increase the cost-efficiency and warrant environmental awareness of waste collection process.

https://www.mdpi.com/1660-4601/16/4/634/pdf

Optimization of Municipal Waste Collection Routing: Impact of Industry 4.0 Technologies on Environmental Awareness and Sustainability.

Garbage classification technology is not only an important basis for the harmless treatment of waste and resource recovery, but also the inevitable trend of social development. The current garbage classification methods rely on manual classification in the garbage collection stage, and it is difficult to achieve satisfying results in consistency, stability, and sanitary conditions. For this reason, this study designs and develops a garbage classification system based on deep learning that can recognize and recycle domestic garbage. Focusing on the problems of low accuracy and poor real-time performance, a lightweight garbage classification model GCNet (Garbage Classification Network) is proposed. GCNet contains three improvements on ShuffleNet v2, including the design of parallel mixed attention mechanism (PMAM), the use of new activation functions, and transfer learning. The experimental results show that the average accuracy of GCNet on the self-built dataset is 97.9%, the amount of model parameters is only 1.3M, the single inference time on Raspberry Pi 4B is about 105ms, and the classification system needs only 0.88 seconds to complete the classification and collection of a single object. The method proposed in this paper is an effective attempt at machine vision in garbage classification and resource recovery. With the improvement of technology, it will effectively promote academic exploration and engineering application in the field of resources and environment. 

Garbage classification system based on improved ShuffleNet v2

Attitude of Chinese public towards municipal solid waste sorting policy: A text mining study.

Residents’ participation in classification and recycling of urban household solid waste (HSW) is a critical factor for the success of municipal solid waste management. The aim of this study is to investigate the decision-making mechanism of residents’ HSW disposal behaviors by merging the theory of planned behavior and the Attitude-Behavior-Condition theory. In this study, based on the survey data of 709 residents in Suzhou, China and structural equation modeling method, the main factors that affect residents’ HSW disposal behaviors and their degree of influence were analyzed, followed by discussion on decision-making mechanisms. The findings show that residents’ behavioral selection has been significantly related to four intrinsic subjective factors and seven external objective factors, and the combined effect of the latter ones is nearly twice of that of the former ones. Moreover, the convenient of environmental facilities and services are most effective at promoting residents’ participation in HSW classification and recycling. Specifically, the observed variables of publicity and education, accessibility to recycling facilities, accessibility to classification facilities, willingness to participation of classification and residents' environmental awareness are the five most significant factors. The impact of laws and regulations is not significant; however, this may be because that there was no mandatory laws, regulations and incentive mechanisms on HSW classification and recycling in Suzhou in this period, and there is still a big gap and room for improvement in this aspect in mainland China. Finally, the study put forward relevant policy recommendations for the comprehensive management of urban HSW classification and recycling.

Investigation on decision-making mechanism of residents’ household solid waste classification and recycling behaviors

Research on the status and priority needs of developing countries to address climate change

This study, using Chongqing City of China as an example, predicts the future motor vehicle population using the Gompertz Model and the motorcycle population using the piecewise regression model, and predicts and analyzes fuel consumption and greenhouse gas (GHG) emissions of motor vehicles from 2016 to 2035 based on the bottom-up method under different scenarios of improving the fuel economy of conventional vehicles, promoting alternative fuel vehicles, and the mixed policy of the above two policy options. The results indicate that the total population of motor vehicles in Chongqing will increase from 4.61 million in 2015 to 10.15 million in 2035. In the business-as-usual scenario, the road-transportation energy demand in Chongqing will keep increasing from 2015 and will peak in 2030, before it begins to decline by 2035. The trends for the tank to wheel (TTW) and well to wheel (WTW) GHG emissions are similar to that of energy demand. The WTW GHG emissions will increase from 24.9 Mt CO2e in 2016 to 50.5 Mt CO2e in 2030 and will then gradually decline to 48.9 Mt CO2e in 2035. Under the policy scenarios of improving fuel economy of conventional fuel passenger cars, promoting alternative fuel vehicles, and their mixed policy, direct energy consumption and TTW and WTW GHG emissions from 2016 to 2035 will be reduced to different levels. It is also found that the two types of policies have a hedging effect on the direct energy-consumption saving, TTW, and WTW GHG emission reductions. Sensitivity analysis of key parameters and policy settings is conducted to investigate the impact of their changes on the vehicle population projection, direct energy demand, and WTW GHG emissions. Some policy implications are suggested to provide reference for the formulation and adjustment of Chongqing’s, or even China’s, low-carbon road transportation policies in the future based on the analysis results.

Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing

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Investigation on decision-making mechanism of residents’ household solid waste classification and recycling behaviors

Research on the status and priority needs of developing countries to address climate change

Scenario Analysis of Urban Road Transportation Energy Demand and GHG Emissions in China—A Case Study for Chongqing