arXiv subjects

Shunsuke Managi

Publications and source records attributed to Shunsuke Managi.

4 recordsLinked to original sources

arXiv2024

Methodology for Calculating CO2 Absorption by Tree Planting for Greening Projects

In order to explore the possibility of carbon credits for greening projects, which play an important role in climate change mitigation, this paper examines a formula for estimating the amount of carbon fixation for greening activities in urban areas through tree planting. The usefulness of the formula studied was examined by conducting calculations based on actual data through measurements made by on-site surveys of a greening companie. A series of calculation results suggest that this formula may be useful. Recognizing carbon credits for green businesses for the carbon sequestration of their projects is an important incentive not only as part of environmental improvement and climate change action, but also to improve the health and well-being of local communities and to generate economic benefits. This study is a pioneering exploration of the methodology.

econ.EM↗

arXiv2018

Modeling the spatio-temporal dynamics of land use change with recurrent neural networks

This study applies recurrent neural networks (RNNs), which are known for its ability to process sequential information, to model the spatio-temporal dynamics of land use change (LUC) and to forecast annual land use maps of the city of Tsukuba, Japan. We develop two categories of RNN models: 1) simple RNN, which is the basic RNN variant, 2) three RNN variants with advanced gated architecture: long short-term memory (LSTM), LSTM with peephole connection (LSTM-peephole), and gated recurrent unit (GRU) models. The four models are developed using spatio-temporal data with high temporal resolution, annual data for the periods 2000 to 2010, 2011 and 2012 to 2016 are used for training, validation and testing, respectively. The predictive performances are evaluated using classification metrics (accuracy and F1 score) and the map comparison metrics (Kappa simulation and fuzzy Kappa simulation). The results show that all RNN models achieve F1 scores higher than 0.55, and Kappa simulations higher than 0.47. Out of the four RNN models, LSTM and LSTM-peephole models significantly outperform the other two RNN models. Furthermore, LSTM-peephole model slightly outperforms the LSTM model. In addition, the results indicate that the RNN models with gated architecture, which have better ability to model longer temporal dependency, significantly outperform the simple RNN model. Moreover, the predictive performance of LSTM-peephole model gradually decreases with the decrease of temporal sequential length of the training set. These results demonstrate the benefit of taking temporal dependency into account to model the LUC process with RNNs.

stat.AP↗

arXiv2018

Enhancement of land-use change modeling using convolutional neural networks and convolutional denoising autoencoders

The neighborhood effect is a key driving factor for the land-use change (LUC) process. This study applies convolutional neural networks (CNN) to capture neighborhood characteristics from satellite images and to enhance the performance of LUC modeling. We develop a hybrid CNN model (conv-net) to predict the LU transition probability by combining satellite images and geographical features. A spatial weight layer is designed to incorporate the distance-decay characteristics of neighborhood effect into conv-net. As an alternative model, we also develop a hybrid convolutional denoising autoencoder and multi-layer perceptron model (CDAE-net), which specifically learns latent representations from satellite images and denoises the image data. Finally, a DINAMICA-based cellular automata (CA) model simulates the LU pattern. The results show that the convolutional-based models improve the modeling performances compared with a model that accepts only the geographical features. Overall, conv-net outperforms CDAE-net in terms of LUC predictive performance. Nonetheless, CDAE-net performs better when the data are noisy.

stat.AP↗

arXiv2006

Analysis of a Japan government intervention on the domestic agriculture market

We investigate an economic system in which one large agent - the Japan government changes the environment of numerous smaller agents - the Japan agriculture producers by indirect regulation of prices of agriculture goods. The reason for this intervention was that before the oil crisis in 1974 Japan agriculture production prices exhibited irregular and large amplitude changes. By means of analysis of correlations and a combination of singular spectrum analysis (SSA), principal component analysis (PCA), and time delay phase space construction (TDPSC) we study the influence of the government measures on the domestic piglet prices and production in Japan. We show that the government regulation politics was successful and leaded (i) to a decrease of the nonstationarities and to increase of predictability of the piglet price; (ii) to a coupling of the price and production cycles; (iii) to increase of determinism of the dynamics of the fluctuations of piglet price around the year average price. The investigated case is an example confirming the thesis that a large agent can change in a significant way the environment of the small agents in complex (economic or financial) systems which can be crucial for their survival or extinction.

physics.soc-ph↗