Mapping of bioclimatic comfort for potential planning using GIS in Aydin

Çetin, Mehmet; Adıgüzel, Fatih; Kaya, Ömer Faruk; Sahap, Ahmet

doi:10.1007/s10668-016-9885-5

Cited by 164 publications

(57 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Moreover, In order to intuitively observe the variation trend of carbon emissions, the amount of carbon emissions are shown in Fig. 1 based on Equation (1).…”

Section: Date Source and Estimationmentioning

confidence: 99%

“……where w is inertia weight coefficient, hence w max and w min refer to the maximum and minimum inertia weight, respectively, which are suggested to be 0.9 and 0.4; t denotes the current iteration number, accordingly t max is the maximum iteration number; c 1 The inertia weight is an important parameter affecting the performance of PSO algorithm, and search and convergence abilities can be effectively controlled. Although LDW strategy has been accepted by the vast majority of researchers, it possesses some defects in the current practical application.…”

Section: A Back Propagation Neural Networkmentioning

confidence: 99%

“…A recent study shows that carbon emissions affect thermal comfort and lead to climate change problems [1], thus affecting natural areas and coastal areas [2][3][4]. As the world's largest emitter of greenhouse gases, China has been taking active measures to cope with climate change.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Carbon Emissions Scenario Prediction of the Thermal Power Industry in the Beijing-Tianjin-Hebei Region Based on a Back Propagation Neural Network Optimized by an Improved Particle Swarm Optimization Algorithm

Zhou¹,

Du²,

Shi³

et al. 2017

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

Rapid economic growth in the Beijing-Tianjin-Hebei region has been accompanied by a dramatic increase in carbon emissions. Therefore, a precise study of forecasting carbon emissions is important as regards curbing them. To identify the influence factors of carbon emissions and effectively predict carbon emissions under the three different GDP growth rate scenarios in the Beijing-Tianjin-Hebei thermal power industry, we employed a combination of the improved particle swarm optimization-back propagation algorithm (IPSO-BP) with scenario prediction. The results are as follows: 1) The influencing degree of carbon emissions factors from strong to weak are the installed capacity of thermal power, thermal power generation, urbanization rate, GDP, and utilization ratio of units (with grey correlation degrees of 0.9262, 0.9247, 0.8683, 0.8082, and 0.7704, respectively). 2) Compared with the BP neural network, it is testified that using the IPSO-BP neural network model with an annual average relative error of 2.53%, while the prediction precision of BP neural network is 5.07%. Besides, the number of iterations to achieve the optimal solution is approximately reduced by 33%. 3) GDP is the contributor to the increment of carbon emissions of the power industry, whereby GDP growth rate can be reduced appropriately to curb carbon emissions, avoiding excessive pursuit of economic growth.

show abstract

“…Moreover, In order to intuitively observe the variation trend of carbon emissions, the amount of carbon emissions are shown in Fig. 1 based on Equation (1).…”

Section: Date Source and Estimationmentioning

confidence: 99%

Section: A Back Propagation Neural Networkmentioning

confidence: 99%

See 1 more Smart Citation

Carbon Emissions Scenario Prediction of the Thermal Power Industry in the Beijing-Tianjin-Hebei Region Based on a Back Propagation Neural Network Optimized by an Improved Particle Swarm Optimization Algorithm

Zhou¹,

Du²,

Shi³

et al. 2017

Pol. J. Environ. Stud.

View full text Add to dashboard Cite

show abstract

“…Human well-being is linked with the ecosystem (i.e., cropland ecosystem) [9,10]. Cropland, one of the most important resources for human beings, is declining in most countries [11].…”

Section: Introductionmentioning

confidence: 99%

A Framework of Payment for Ecosystem Services to Protect Cropland: A Case Study of the Yangtze River Delta in China

et al. 2018

View full text Add to dashboard Cite

Abstract:Due to large land demand and insufficient economic incentives, stakeholders have little motivation to protect cropland during rapid urbanization. The considerable loss of cropland poses a serious threat to food security and ecological sustainability. This research proposes a framework of payment for ecosystem services (PES) to reconcile the large land demand and the need for cropland protection during economic development by identifying whom to compensate, what to pay, how much to pay, the mechanisms for payment, and verification of service delivery. Using the Yangtze River Delta as an example, the features of the PES based on supply-demand analysis, compensation quality and value standards, and payment regulation are demonstrated. The results show the effectiveness of this PES framework for handling the externality of environmental protection compared to traditional regulatory approaches. The framework will also aid in the protection of cropland by coordinating the benefits of stakeholders.

show abstract

“…This leads to climate change on the environment as well as forested, coastal, and urban areas [13][14][15]. A recent study shows that carbon emissions affect thermal comfort and lead to the climate change problem [16].…”

mentioning

confidence: 99%