Search

Intensive urban land use is a strategy to enhance productivity and mitigate environmental challenges in emerging regions, but its relationship with carbon emissions needs further city-level investigation. This study investigates the impact of intensive urban land use on carbon emissions across 153 cities in China, thus employing the STIRPAT model with the ordinary least square (OLS) and geographical weighted regression (GWR) methods. The findings underscore the heterogenous influence of intensive urban land use on carbon emissions across China's urban landscapes: (1) R&D investment intensity and population density show significant negative association with carbon emissions in general. (2) Capital investment intensity positively affects carbon emissions in low-income cities, R&D investment intensity shows negative effects on carbon emissions in middle-income cities, and population density emerges as a substantial factor in reducing carbon emissions in both middle- and low-income cities. (3) Capital intensity, labor intensity, and R&D investment intensity exert positive effects on emissions in middle China and negative influences in northeastern and southern China, whereas population density shows converse spatial effects. Based on the study's results, tailored policy implications are provided for urban planning authorities in emerging regions. [ABSTRACT FROM AUTHOR]

More than 80% of energy is consumed during operation phase of a building's life cycle, so energy efficiency retrofit for existing buildings is considered a promising way to reduce energy use in buildings. The investment strategies of retrofit depend on the ability to quantify energy savings by “measurement and verification” (M&V), which compares actual energy consumption to how much energy would have been used without retrofit (called the “baseline” of energy use). Although numerous models exist for predicting baseline of energy use, a critical limitation is that occupancy has not been included as a variable. However, occupancy rate is essential for energy consumption and was emphasized by previous studies. This study develops a new baseline model which is built upon the Lawrence Berkeley National Laboratory (LBNL) model but includes the use of building occupancy data. The study also proposes metrics to quantify the accuracy of prediction and the impacts of variables. However, the results show that including occupancy data does not significantly improve the accuracy of the baseline model, especially for HVAC load. The reasons are discussed further. In addition, sensitivity analysis is conducted to show the influence of parameters in baseline models. The results from this study can help us understand the influence of occupancy on energy use, improve energy baseline prediction by including the occupancy factor, reduce risks of M&V and facilitate investment strategies of energy efficiency retrofit.

Occupants are a critical impact factor of building energy consumption. Numerous previous studies emphasized the role of occupants and investigated the interactions between occupants and buildings. However, a fundamental problem, how to learn occupancy patterns and predict occupancy schedule, has not been well addressed due to highly stochastic activities of occupants and insufficient data. This study proposes a data mining based approach for occupancy schedule learning and prediction in office buildings. The proposed approach first recognizes the patterns of occupant presence by cluster analysis, then learns the schedule rules by decision tree, and finally predicts the occupancy schedules based on the inducted rules. A case study was conducted in an office building in Philadelphia, U.S. Based on one-year observed data, the validation results indicate that the proposed approach significantly improves the accuracy of occupancy schedule prediction. The proposed approach only requires simple input data (i.e., the time series data of occupant number entering and exiting a building), which is available in most office buildings. Therefore, this approach is practical to facilitate occupancy schedule prediction, building energy simulation and facility operation.

Undervaluing the stakeholders' attributes, salience, and potential to impact a project during its planning and execution may result in stakeholders' dissatisfaction, distrust, and opposition, leading to project controversies, cost overrun, schedule delays, and even project cessation. The existing stakeholders' management typologies due to their inherent limitations are unable to provide the project managers with an optimal and comprehensive solution. The present study proposes a framework to improve the stakeholders' management process by a novel way of mapping stakeholders' attribute-based salience and potential impact probability into a dynamic stakeholder relational matrix. The framework was validated through a case study conducted on a mega-highway project from China-Pakistan Economic Corridor. The data was collected through a questionnaire survey and analyzed using SPSS. Twelve stakeholder groups with 36 stakeholders were identified. Stakeholders' salience index and stakeholders' impact probability were computed and mapped in the stakeholders' salience assessment matrix (SSAM). The findings revealed significant dominance of the political hierarchy, project management, and defense services in the alignment selection process. Environmentalists, community, local authority, and non-governmental organizations were found deprived of reasonable participation opportunities, and their presence is often undermined and neglected in the selection process. However, the logical stakeholders' classification and corresponding relational and engagement strategies offered by SSAM are expected to compensate the disparity and improve transparency in the decision process. This study contributes to the existing body of knowledge by proposing a comprehensive framework that integrates stakeholders' salience, potential impact, and relational strategy simultaneously. The framework is expected to aid project managers during crucial project decision-making stages to assess stakeholders, their participation provisos, and desired engagement approaches. The proposed framework exhibits the requisite flexibility for its application on diverse infrastructure projects with certain project-specific modifications. Key words: stakeholder, highway alignment, salience, China-Pakistan Economic Corridor (CPEC), decision-making. La sous-evaluation des attributs, du pouvoir d'influence et du potentiel des parties prenantes d'avoir une incidence sur un projet au cours de sa planification et de son execution peut entrainer l'insatisfaction, la mefiance et l'opposition des parties prenantes, ce qui peut mener a des controverses, a des depassements de couts, a des retards d'execution et meme a la cessation du projet. Les typologies de gestion des parties prenantes existantes en raison de leurs limites inherentes ne sont pas en mesure de fournir aux gestionnaires de projet une solution optimale et complete. La presente etude propose un cadre visant a ameliorer le processus de gestion des parties prenantes par une nouvelle facon de cartographier la preponderance des attributs des parties prenantes et la probabilite d'impact potentiel dans une matrice relationnelle dynamique des parties prenantes. Le cadre a ete valide au moyen d'une etude de cas realisee sur un mega projet routier du corridor economique Chine-Pakistan. Les donnees ont ete recueillies au moyen d'un questionnaire et analysees a l'aide de SPSS. Douze groupes de parties prenantes, incluant 36 parties prenantes, ont ete identifies. L'indice de preponderance et la probabilite d'impact des parties prenantes ont ete calcules et cartographies dans la matrice d'evaluation de la preponderance des parties prenantes (<< stakeholders salience assessment matrix, SSAM >>). Le resultat a revele une predominance importante de la hierarchie politique, de la gestion de projet et des services de defense dans le processus de selection de l'alignement. Les environnementalistes, les collectivites, les autorites locales et les ONG ont ete prives de possibilites raisonnables de participation, et leur presence est souvent compromise et negligee dans le processus de selection. Toutefois, la classification logique des parties prenantes et les strategies relationnelles et de mobilisation correspondantes offertes par la SSAM devraient compenser la disparite et ameliorer la transparence du processus decisionnel. Cette etude contribue a l'ensemble des connaissances existantes en proposant un cadre complet qui integre simultanement l'importance des parties prenantes, leur impact potentiel et leur strategie relationnelle. Le cadre devrait aider les gestionnaires de projet a evaluer les parties prenantes, les conditions de leur participation et les approches de mobilisation souhaitees au cours des etapes cruciales du processus decisionnel. Le cadre propose presente la souplesse requise pour son application a divers projets d'infrastructure avec certaines modifications propres a chaque projet. [Traduit par la Redaction] Mots-cles: partie prenante, trace routier, importance (<< salience >>), corridor economique Chine-Pakistan (CECP), prise de decision., Introduction Effective and efficient stakeholder management is an essential element of project management (Aaltonen 2010). The diversity of project stakeholders often results in conflicting values and objectives, thus creating the [...]