Hasil untuk "Land use"

N. Alexandratos, J. Bruinsma

K. Seto, Burak Güneralp, L. Hutyra

S. Angel, J. Parent, D. Civco et al.

H. Godfray, J. Beddington, I. Crute et al.

N. Ramankutty, A. Evan, C. Monfreda et al.

S. Sitch, C. Huntingford, N. Gedney et al.

M. Tewari, F. Chen, W. Wang et al.

R. DeFries, J. Townshend

C. Tucker, J. Townshend, T. Goff

M. Kanamitsu, W. Ebisuzaki, J. Woollen et al.

Hanqiu Xu

The normalized difference water index (NDWI) of McFeeters (1996) was modified by substitution of a middle infrared band such as Landsat TM band 5 for the near infrared band used in the NDWI. The modified NDWI (MNDWI) can enhance open water features while efficiently suppressing and even removing built‐up land noise as well as vegetation and soil noise. The enhanced water information using the NDWI is often mixed with built‐up land noise and the area of extracted water is thus overestimated. Accordingly, the MNDWI is more suitable for enhancing and extracting water information for a water region with a background dominated by built‐up land areas because of its advantage in reducing and even removing built‐up land noise over the NDWI.

C. Priestley, R. Taylor

Gene Bazan

Review: Our Ecological Footprint: reducing human impact on the Earth. By Mathis Wackernagel and William Rees Reviewed by Gene Bazan Center for Sustainability, Pennsylvania State University Wackernagel, Mathis and William Rees. Our Ecological Footprint: reducing human impact on the Earth. Philadelphia, PA: New Society Publishers, 1996. 160 pp. US $14.94 paper ISBN: 0-86571-312-X. Partially recycled, acid-free paper using soy-based ink. If the earth's inhabitants were to live at the standard of the U.S., we would require three planet Earths to support us. Many of us have heard or read something like this before. Our Ecological Footprint provides a graphically compelling and quantitatively rigorous way for us to engage in the worldwide sustainability debate: Ecological Footprint analysis. Through this analysis we can determine the consequences of our behavior, and proposed solutions, at any level: individual, household, community, nation, or world. Ecological Footprint analysis measures the aggregate land area required for a given population to exist in a sustainable manner. Wackernagel and Rees note that at 11 acres per person, the U.S. has the highest per capita footprint and suggest that this number should be closer to 6 acres per person. Further, the U.S. faces an 80% ecological deficit, which means we are borrowing from our grandchildren's legacy, and expropriating land from elsewhere in the world. By contrast, each European requires around 5 acres; however, Europeans face higher ecological deficits because they have smaller land areas. Unlike other approaches, which focus on the depletion of non-renewables such as fossil fuel and minerals, Ecological Footprint analysis asserts that the road to sustainability must be paved with sustainable practices. Thus, our use of fossil fuel must have as a compensatory sink the acres of woodlot required to sequester the carbon from our combustion of fossil fuel (in our cars, home heating, etc.) or, alternatively, the acres of fields required to grow biofuel. For example, in comparing our daily commute by car, bus or bicycle, and considering all land requirements (e.g., manufacturing land to produce

T. Besley

D. Massonnet, M. Rossi, Cesar Carmona et al.

Zhi Wang, Wei Ma, Yunfei Lu et al.

Biochar has been widely applied as an efficiency soil additive to modify the quality of cultivated field. However, the effects of long-term biochar addition on spatial and temporal dynamics of soil compaction, and the changes in soil moisture condition and plant root growth remain unclear. Hence, an eight-year (2015/16–2023/24) consecutive field experiment on wheat was conducted in the subtropical humid region of east China, using three treatments: no N fertilizer (PK), chemical fertilizer (NPK), NPK plus biochar (5 t ha−1 yr−1, NPKB). Relative to NPK, across nine growing seasons of wheat, NPKB decreased the soil bulk density by 0.019 and 0.013 units (g cm−3 yr−1), and decreased the soil penetration resistance by 0.028 and 0.015 units (MPa yr−1) in 0–10 cm and 10–20 cm depths, respectively. Biochar addition improved soil water content from seeding to flowering, increased wheat root distribution during the whole growth period, and enhanced soil N supply capacity by promoting N adsorption, which gave rise to greater biomass and N accumulation and more biomass allocation in grain. As a result, NPKB increased wheat yield by 14.8 %, N recovery efficiency by 55.1 %, and crop water productivity by 14.9 %, relative to NPK, on average across four growing seasons of wheat. Therefore, long-term biochar addition has potential to substantially increase grain yield of post-rice wheat, water productivity, and N recovery efficiency. Hence, for the sustainable intensification cropping in the long-run, successive biochar addition could be a finable management for wheat production on the rainfed Yangtze River Region of China.

Jing Wu, Yawei Wang, Gaizhong Chen et al.

As a famous coastal tourist city in China, Sanya is facing the dual challenges of solid waste management and resource utilization while tourism is booming. To realize efficient solid waste management and innovative circular economy models, Sanya actively explores and practices the construction path of a “zero-waste city”. In this study, Pearson correlation analysis and material flow analysis were used to analyze the factors influencing the amount of municipal solid waste (MSW) generated in Sanya and the changes in the effectiveness of MSW treatment in Sanya before the construction of the “zero-waste city” (2018) and five years later (2023). The results of the study show that the construction of a “zero-waste city” in Sanya, through the implementation of a series of policy measures, including the strengthening of strategic planning and leadership, the upgrading of capacity building, and the promotion of nationwide action participation, has effectively promoted the efficient synergistic treatment of MSW, thereby realizing both environmental benefits and economic benefits.

Pablo J. Mira

"Viajar al Futuro" de Walter Sosa Escudero es un libro divulgativo que explora la ciencia detrás de los pronósticos, equilibrando rigor científico y anécdotas. El autor, experto en estadística y big data, destaca la importancia de entender la estadística en un mundo incierto y cómo su aplicación consciente impacta decisiones personales y políticas públicas. 

Jeremy Nemeth, J. Langhorst

Salaheddin Manochehri, Fateh Habibi

Purpose: During the last two decades, housing price fluctuations in some countries including Iran have been a main challenge of the housing market and the country's economy. In one period, there was a significant increase in housing prices and, in another period, it decreased or stabilized. Relatively high and widespread, it governs the price of housing, as a result of which significant developments have occurred in the housing sector and in the entire economy. In new theories, housing prices can fluctuate over time, and housing price fluctuations can be divided into two important categories. First, minor fluctuations result from market structure based on fundamentals. The housing market is based on the housing supply and demand conditions and the endogenous factors of the housing sector. Hence, the gradual and slow changes in the housing price over time are caused by the basic and underlying factors of the housing market and through changes in the total cost. Housing production changes housing prices. Second, housing cyclical shocks or impulses, are the exogenous factors that create cyclical shocks in the housing sector, and the monetary policy's effect on asset prices, including real estate and housing, is determined. The capital market, household asset portfolio composition and macroeconomic variables are among them.Methodology: We assume thatis the probability space,  is a filter created by Brownian  and Poisson process  with  is intensity. We also assume that Brownian process, Poisson process  and price jump  are independent of one another.  housing prices are based on time . In the Black-Scholes model (BSM), housing prices at time t are modeled by the following geometric Brownian process:where  is the average and  standard deviation of housing prices. In the jump diffusion model (JDM), housing prices are calculated by the following equation:where  is the expected growth rate,  is the turbulence of the Brownian process, and  is the housing price at time t and before the jump.Results and discussion: In this research, using GEM algorithm, the five parameters of jump diffusion model were estimated and then two parameters of Black-Scholes model were estimated using the maximum likelihood method. Next, the simulation of the future housing price was done based on the Monte-Carlo method. The simulation was done in 100,000 repetitions, and then the best model was selected. The housing price was simulated based on the real price, so that the price at time t could be calculated with its next monthly price, i.e. t+1. This method was repeated until the last data. In this research, many models were simulated with random numbers generated for housing prices to get the best model with the least error. In three cases of 6 months, 12 months and 24 months, housing prices were simulated and predicted. One way to calculate the accuracy of the model was based on the confidence interval with the assumption of normal approximation. One way to check the stability of the obtained coefficients of the models was to repeat the simulation with different random numbers and calculate the average performance of each model. In this research, in order to avoid bringing a large number of estimated models, 25 models with the best performance and the least error, and among these 25 models, the best models were identified.The results of the models show that, in most of the provincial centers of Iran, the jump diffusion model yields better results than the Black-Scholes model. Also, in some provincial centers, the 6-month performance is better, and, in some others, 12-month or 24-month performance is better. On the other hand, some provincial centers perform better in 6 months, 12 months and 24 months. The results of the average jump frequency in the centers of the provinces of Iran in the housing market show that, for most of the provinces, the average jump frequency is a high number, which indicates high fluctuations and the high impact of internal and external shocks in the Iranian housing market.Conclusions and policy implications: Accurate modeling of the pricing of various assets, including the housing market, as well as its fluctuations, has always been one of the concerns of researchers and policymakers. Therefore, this research aimed at the comparative analysis of housing prices using Black-Scholes asset pricing models and jump diffusion in the provincial centers of Iran. This study used the monthly housing price data in the provincial centers of Iran for a period from March 2009 to March 2023. In addition, through the GEM algorithm, the jump diffusion model and the maximum likelihood method, the Black-Scholes model was fulfilled, and then the future housing prices in the centers of the provinces of Iran were simulated by the Monte Carlo method. The research results show that, in most provinces of Iran, the jump diffusion model has better and more accurate results than the Black-Scholes model in 6, 12 and 24 months of performance. It is worth mentioning that, in some provincial centers, the results of the Black-Scholes model were better than the jump diffusion model. According to the results of the average jump frequency, it is clear that the highest and lowest average jump frequencies belong to Khorasan Razavi and Kohgiluyeh-Boyer Ahmad Provinces with values of 0.58 and 0.09, respectively.