Welcome to Scholar Publishing Group

Nature Environmental Protection, 2022, 3(3); doi: 10.38007/NEP.2022.030302.

Urban and Rural Natural Environment Protection Policies under Big Data

Author(s)

Jiwei Zhang

Corresponding Author:
Jiwei Zhang
Affiliation(s)

Gansu Industry Polytechnic College, Gansu, China

Abstract

As the country attaches great importance to environmental protection and sustainable development in recent years, and people aspire to a better living environment with green mountains and water, it has become an important and urgent task to restore green mountains and water and accelerate the process of ecological construction through environmental management, which is also a basic principle that must be adhered to in order to implement the national policy of environmental protection in China. The aim of this paper is to study urban and rural natural environment protection policies in the context of big data. The paper discusses the definition of big data theory and urban and rural environment, constructs an evaluation system for the implementation of urban and rural natural environmental protection policies, and conducts an empirical study using District M as an example, concluding that the reasons for the ineffective implementation of policies in District M are complex and diverse, mainly including the policy quality of environmental protection policies needs to be improved and the implementation environment is unsatisfactory.

Keywords

Big Data Technology, Urban and Rural Environment, Environmental Protection, Policy Analysis

Cite This Paper

Jiwei Zhang. Urban and Rural Natural Environment Protection Policies under Big Data. Nature Environmental Protection (2022), Vol. 3, Issue 3: 9-17. https://doi.org/10.38007/NEP.2022.030302.

References

[1] Tobias E, Martin P, Doreen T. Automation of Maritime Shipping for More Safety and Environmental Protection. Autom. (2022) 70(5): 406-410. https://doi.org/10.1515/auto-2022-0003

[2] Eleni S. A, Evangelos N. G, Konstantina S. Nikita. A Safety System for Human Radiation Protection and Guidance in Extreme Environmental Conditions. IEEE Syst. J. (2020) 14(1): 1384-1394. https://doi.org/10.1109/JSYST.2019.2920135

[3] Sivachandran V, Malleswaran M. Performance Analysis of Energy-Efficient Cellular Networking on Urban and Rural Environments. Wirel. Pers. Commun. (2018) 103(4): 3113-3126. https://doi.org/10.1007/s11277-018-5997-6

[4] Waleed A, Saleh A. Big Data Analytics: Deep Content-Based Prediction with Sampling Perspective. Comput. Syst. Sci. Eng. (2023) 45(1): 531-544. https://doi.org/10.32604/csse.2023.021548

[5] Oluwasegun T O, Antonio F A, Rosa E. Lillo, C S. Detecting and Classifying Outliers in Big Functional Data. Adv. Data Anal. Classif. (2022) 16(3): 725-760. https://doi.org/10.1007/s11634-021-00460-9

[6] Konstantinos V. Katsikopoulos, M C. Canellas. Decoding Human Behavior with Big Data? Critical, Constructive Input from the Decision Sciences. AI Mag. (2022) 43(1): 126-138. https://doi.org/10.1609/aimag.v43i1.7381

[7] Raúl E. Cybersyn, Big Data, Variety Engineering and Governance. AI Soc. (2022) 37(3): 1163-1177. https://doi.org/10.1007/s00146-021-01348-0

[8] Marion M. The future of urban models in the Big Data and AI era: a bibliometric analysis (2000-2019). AI Soc. (2022) 37(1): 177-194. https://doi.org/10.1007/s00146-021-01166-4

[9] Adrian B, Acela T G, Rosa M C C. Real-Time Big Data Architecture for Processing Cryptocurrency and Social Media Data: A Clustering Approach Based on k-Means. Algorithms (2022) 15(5): 140. https://doi.org/10.3390/a15050140

[10] Clarisse D, Laetitia J. Metaheuristics for Data Mining: Survey and Opportunities for Big Data. Ann. Oper. Res. (2022) 314(1): 117-140. https://doi.org/10.1007/s10479-021-04496-0

[11] Varun M, Florian K, Jan N K, Elgar F, Tobias K, David K. Detecting Receptivity for mHealth Interventions in the Natural Environment. Proc. ACM Interact. Mob. Wearable Ubiquitous Technol. (2021) 5(2): 74:1-74:24. https://doi.org/10.1145/3463492

[12] Laura A, Ruochen L, Rajiv K, Hejamadi R R. Effects of Structural and Trait Competitiveness Stimulated by Points and Leaderboards on User Engagement and Performance Growth: A Natural Experiment with Gamification in an Informal Learning Environment. Eur. J. Inf. Syst. (2020) 29(6): 704-730. https://doi.org/10.1080/0960085X.2020.1808540 

[13] Ali A, Roger S C, Roozbeh J, Bobak J M. Using Intelligent Personal Annotations to Improve Human Activity Recognition for Movements in Natural Environments. IEEE J. Biomed. Health Informatics (2020) 24(9): 2639-2650. https://doi.org/10.1109/JBHI.2020.2966151

[14] Gábor K, Yasuharu K, Takao M, Hideki H. Saliency and Spatial Information-Based Landmark Selection for Mobile Robot Navigation in Natural Environments. Adv. Robotics (2019) 33(10): 520-535. https://doi.org/10.1080/01691864.2019.1602564

[15] Alice C, Andrea G: When Virtual Feels Real. Comparing Emotional Responses and Presence in Virtual and Natural Environments. Cyberpsychology Behav. Soc. Netw. (2019) 22(3): 220-226. https://doi.org/10.1089/cyber.2018.0393

[16] Dina K, Suzan A, Birsen D, Martina R, Joyita C. Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand. Int. J. Mob. Hum. Comput. Interact. (2019) 11(3): 59-70. https://doi.org/10.4018/IJMHCI.2019070104

[17] Euisung J, Eun J J. Service-Oriented Architecture of Environmental Information Systems to Forecast the Impacts of Natural Disasters in South Korea. J. Enterp. Inf. Manag. (2019) 32(1): 16-35. https://doi.org/10.1108/JEIM-03-2015-0022

[18] George M, Stavroula N, Margherita A, Constantine S. Augmenting Natural Interaction with Physical Paper in Ambient Intelligence Environments. Multim. Tools Appl. (2019) 78(10): 13387-13433. https://doi.org/10.1007/s11042-018-7088-9